猪肉肉糜品质与安全可见/近红外光谱快速检测方法的实验研究
|
摘要
肉及肉制品是人类获得蛋白质、维生素和矿物质等营养成分的重要来源之一,猪肉作为我国生产和消费最大的畜肉产品,已成为人们饮食结构的基本组成部分。随着人们生活水平的提高和膳食结构的改变,对猪肉品质与安全提出了更高的要求。我国作为猪肉生产消费大国,但猪肉产品却难以进入国际市场,猪肉品质检测技术和评价方法繁琐,降低了监管机构的工作效率,因此实现猪肉品质与安全的快速检测和评价已成为猪肉行业发展和保证食品安全的重要环节之一
本研究以新鲜猪肉肉糜为研究对象,利用可见/近红外光谱分析技术、现代仪器分析技术和化学计量学方法,开展了猪肉营养品质和安全品质的快速检测研究,主要对猪肉肉糜中粗脂肪、肌内脂肪、蛋白质、水分和14种脂肪酸进行了定量检测研究,以及不同部位肌肉肉糜的定性鉴别分析研究；针对安全品质检测,.对猪肉肉糜掺假检测和新鲜度的检测进行了研究,建立了掺假和新鲜度评价方法,对不同建模方法、不同光谱预处理和不同仪器进行了比较分析。
主要研究结论如下：
(1)研究了新鲜猪肉肉糜品质指标的定量分析。采用便携式光谱仪对猪肉背最长肌样本的肌内脂肪、蛋白质和水分含量进行了定量检测,比较了SMLR、PLSR和LS-SVM三种定量方法,结果表明SMLR模型性能整体较差,PLSR和LS-SVM两种方法结果较好,原始光谱建立的3个指标的PLSR模型相对最优,OSC光谱预处理后建立的3个指标的LS-SVM模型相对较优,两种方法建立的肌内脂肪、蛋白质和水分含量的相对较优模型性能差异不大。进一步采用不同光谱仪对肌内脂肪含量的定量分析进行了研究,基于傅立叶变换近红外光谱所建PLSR和LS-SVM模型结果差异不大,基于便携式光谱仪采集可见/近红外光谱所建PLSR模型结果优于LS-SVM模型,傅立叶变换近红外光谱所建模型性能优于便携式光谱仪采集的可见/近红外光谱所建模型；相对最优PLSR模型的校正集和预测集相关系数分别为0.964、0.960,RMSEC和RMSEP分别为0.249和0.491,相对最优LS-SVM模型的校正集和预测集相关系数分别为0.966、0.960,RMSEC和RMSEP分别为0.249和0.444。为扩大品质指标含量的范围,对采集至猪肉不同部位肌肉的肉糜样本的品质组分定量分析进行了研究,比较了PLSR和LS-SVM两种定量校正方法对脂肪、蛋白质和水分含量的检测,对脂肪、蛋白质和水分含量的LS-SVM模型整体好于PLSR模型；对脂肪含量的分析基于傅立叶变换近红外光谱建立的模型优于基于便携式光谱仪采集光谱建立的模型,对蛋白质和水分含量的分析模型基于USB4000采集光谱建立的模型较优；综合分析脂肪、蛋白质和水分含量最优分析模型,脂肪和水分含量模型的校正及预测相关系数均高于0.9,模型的预测精度及稳定性均较好,但蛋白质含量模型校正及预测相关系数仅高于0.7,模型精度相对较差。
(2)研究了不同部位猪肉肉糜的定性判别分析。主要对4种不同肌肉类型的定性分析,比较了不同仪器,不同定性分析方法和不同光谱预处理所建鉴别模型的性能。不同光谱预测对模型性能产生的影响,结果表明原始光谱或一阶微分光谱所建模型的性能较优；不同光谱采集仪器的对比结果表明,便携式光谱仪获得的可见/近红外光谱更适用于不同肌肉类型猪肉肉糜的定性鉴别分析；比较不同的定性分析方法,DA方法所建模型性能略差,PLSDA最优模型校正及预测判别正确率分别为100%和94%,LA-SVMDA最优模型校正及预测判别正确率分别为99%和98%,表明可见/近红外光谱分析可快速鉴别不同部位猪肉肉糜。
(3)对猪肉肉糜脂肪酸含量进行了定量检测研究。利用可见/近红外光谱分析技术对猪肉背最长肌中14种脂肪酸含量的定量预测进行了分析,主要比较了不同便携式光谱仪获得的不同可见/近红外光谱建模、PLSR和LS-SVM两种不同建模方法及不同光谱预处理所建立的模型结果,两种不同的光谱仪和两种不同的定量分析方法对脂肪酸含量的检测没有显示出明显的优势,只是对某些指标的检测结果有相对较好的预测结果：比较C14：0、C16:0、C16:1、C17:0、C18:0、C18:1、C18:2、C18:3、C20:1、C20:4、C20:5、SFA、MUFA和PUFA的相对较优定量模型,其中C 17：0、C18：3和SFA模型相关性、模型精度及稳定性较优,C20：4、C20：5和MUFA模型性能性对略差,各指标检测精度需要进一步提高。
(4)对猪肉肉糜掺假检测进行了研究。采用傅立叶变换近红外光谱仪和便携式光谱仪USB4000获得同源掺假和非同源掺假两类样本不同等级掺假的可见/近红外光谱,建立不同等级掺假的定量分析模型。比较PLSR、PCR和MLR三种定量分析方法对不同等级掺假检测的建模结果,模型的总体性能差异不大,考虑到模型的稳定性、最优精度和适应性,比较不同波段、不同方法、不同掺假类型的模型性能,便携式光谱仪的检测结果较好,基于多元线性回归模型的性能较好,对同源掺假检测,基于可见/近红外光谱的MLR最优模型,校正预测及交互验证相关系数r分别为0.965、0.958和0.949,RMSEC、RMSEP和RMSECV分别为0.083、0.092和0.100；对非同源掺假检测,基于可见/近红外波段的MLR模型性能最优,校正预测及交互验证相关系数r分别为0.961、0.971和0.945,RMSEC、RMSEP和RMSECV分别为0.087、0.078和0.104,模型最稳定,预测精度最好,适用于猪肉肉糜掺假的快速检测。
(5)研究了猪肉新鲜度的定量评价方法。利用便携式光谱仪扫描采集保存不同时间猪肉的可见/近红外光谱,采用SMLR, PCR和PLSR三种定量分析方法建立新鲜度的预测模型,比较不同预处理对模型性能的影响,SMLR模型总体性能最差,PCR模型总体性能次之,PLSR模型的总体性能较优,3点光谱建立的PLSR模型结果最优,校正预测及交互验证相关系数r分别为0.970、0.956和0.887,RMSEC、RMSEP和RMSECV分别为2.63 mg/100g、3.45 mg/100g和5.01mg/100g。研究结果表明,可见/近红外光谱分析技术能实现对猪肉新鲜度的快速检测,对实现监测猪肉品质安全提供了保障和依据。
Meat and meat products are one of the essential sources of human nutritional component, such as protein, vitamins and minerals. Pork is one of the main components of human diet, which is the largest production and consumption in China. Along with improvement of living standard and changing of meal structure, the demand for pork quality and safety should be higher than before. China is a major pork producer as well as a major pork consumer, but not powerful in international market. Testing technology and assessment method have complicated formalities, which reduce the efficiency of regulatory authority. Therefore, rapid detection and evaluation of pork quality and safety become one of important part in the development of pork industry and the assurance of food safety.
The research object is fresh minced pork. Detection of pork quality and safety were carried out based on visible/near infrared (NIR) spectroscopy, chemometrics techniques combined with modern physical-chemical analysis techniques. Quantitative models were established based on visible/NIR spectra for fat, intramuscular fat, protein, moisture and fatty acids determination, and qualitative models were also established for muscle discriminant from different part of pig. In this dissertation, detection of adulteration and freshness using visible/NIR spectra were also studied for safe quality detection of pork.
The main results and conclusions were:
(1) Quantitative analysis of minced pork quality was studied. Intramuscular fat, protein, moisture content in minced pork form longissimus dorsi muscle were analyzed quantitatively based on visible/NIR spectra by a portable instrument. Comparison results of different calibration methods of stepwise multi linear regression (SMLR), partial least squares regression (PLSR), least squares support vector machines (LS-SVM) and different spectra pretreatments showed that the performance of PLSR and LS-SVM model was much better than SMLR model. The best model of PLSR was established based on the raw spectra, and the best models of LS-SVM was based on the spectra with orthogonal signals correction (OSC) pretreatment for intramuscular fat, protein and moisture content, which was closed to PLSR calibration performance. Further research for quantitative analysis of Intramuscular fat was studied. Comparison results of different calibration methods of PLSR, LS-SVM and different spectra pretreatments and different detectors showed that the performance of model based on the spectra acquired by InGaAs detector was much better. The performance of LS-SVM model was close to that of PLSR model, but the performance of PLSR model based on the spectra acquired by USB4000 was much better than the LS-SVM model. For the best PLSR model, the correlation coefficients of calibration and validation were 0.964 and 0.960, respectively; the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.249and 0.491, respectively. For the best LS-SVM model, the correlation coefficients of calibration and validation were 0.966 and 0.960, respectively; RMSEC and RMSEP were 0.249 and 0.444, respectively. In order to expanding the range of the content for quality index, the samples of minced pork collected from different muscle of pig were analyzed quantitatively. Comparison results of different calibration methods of PLSR, LS-SVM and different spectra pretreatments and different detectors showed that the performance of LS-SVM model was better than PLSR model for fat, protein and moisture content. For fat, the model based on the spectra acquired by InGaAs detector performed better. For protein and moisture, the model based on the spectra acquired by USB4000 performed better. The correlation coefficients for calibration and validation were above 0.9 for detection of fat and moisture, and the precision and stability were strong. But for the protein, the correlation coefficient for calibration and validation were above 0.7, the precision was relatively worse.
(2) Qualitative analysis of minced pork from 4 different muscle was studied. Comparison results of different calibration methods of discriminant analysis (DA), partial least squares discriminant analysis (PLSDA), least squares support vector machines discriminant analysis (LS-SVMDA) and different spectra pretreatments and different detectors showed that the model based on the raw spectra or first derivative performed better. Visible/NIR spectra obtained by portable instrument were more suitable for discrimination of 4 different muscles. The discriminant results of DA model were a bit worse than PLSDA model and LA-SVMDA model. The discriminant accuracy of calibration and validation for the best PLSDA model were 100% and 94%, respectively and for LA-SVMDA were 99% and 98%, respectively.
(3) Quantitative analysis of fatty acids was researched. Comparison results of different calibration methods of PLSR, LS-SVM and different spectra pretreatments and different detectors for detection of fatty acid content showed that there were no significant difference between PLSR and LS-SVM or two different detectors as a whole. The correlations, precision and stability were relatively better for C17:0, C18:3 and SFA, but worse for C20:4, C20:5 and MUFA. The precision of quantitative analysis for fatty acids should be improved.
(4) Quantitative analysis of adulteration in minced pork were studied with different grade adulteration based on different detectors. Comparison results of different calibration methods and different wavebands and different type adulteration showed that the performance of multi linear regression (MLR) model based on visible/NIR spectra was much better, the correlation coefficients of calibration, validation and cross validation were 0.965,0.958 and 0.949, respectively; RMSEC, RMSEP and RMSECV were 0.083,0.092 and 0.100, respectively for adulterated with the same homologous material, and the correlation coefficients of calibration, validation and cross validation were 0.961,0.971 and 0.945, respectively; RMSEC, RMSEP and RMSECV were 0.087,0.078 and 0.104, respectively for adulterated with the nonhomologous material.
(5) Quantitive analysis of freshness of pork were studied. The models established using SMLR, PCR and PLSR based on the visible NIR spectra. Comparison results of different calibration methods and different spectra pretreatment showed that the performance of PLSR model was much better and the SMLR model was much worse. The best model using first derivative spectra achieved the correlation coefficients of calibration, validation and cross validation were 0.970,0.956 and 0.887, respectively; RMSEC, RMSEP and RMSECV were 2.63 mg/100g,3.45 mg/100g and 5.01 mg/100g, respectively. This study demonstrated that visible/NIR spectroscopy can be successfully applied as a rapid method to determine the quality and safety of pork.
本研究以新鲜猪肉肉糜为研究对象,利用可见/近红外光谱分析技术、现代仪器分析技术和化学计量学方法,开展了猪肉营养品质和安全品质的快速检测研究,主要对猪肉肉糜中粗脂肪、肌内脂肪、蛋白质、水分和14种脂肪酸进行了定量检测研究,以及不同部位肌肉肉糜的定性鉴别分析研究；针对安全品质检测,.对猪肉肉糜掺假检测和新鲜度的检测进行了研究,建立了掺假和新鲜度评价方法,对不同建模方法、不同光谱预处理和不同仪器进行了比较分析。
主要研究结论如下：
(1)研究了新鲜猪肉肉糜品质指标的定量分析。采用便携式光谱仪对猪肉背最长肌样本的肌内脂肪、蛋白质和水分含量进行了定量检测,比较了SMLR、PLSR和LS-SVM三种定量方法,结果表明SMLR模型性能整体较差,PLSR和LS-SVM两种方法结果较好,原始光谱建立的3个指标的PLSR模型相对最优,OSC光谱预处理后建立的3个指标的LS-SVM模型相对较优,两种方法建立的肌内脂肪、蛋白质和水分含量的相对较优模型性能差异不大。进一步采用不同光谱仪对肌内脂肪含量的定量分析进行了研究,基于傅立叶变换近红外光谱所建PLSR和LS-SVM模型结果差异不大,基于便携式光谱仪采集可见/近红外光谱所建PLSR模型结果优于LS-SVM模型,傅立叶变换近红外光谱所建模型性能优于便携式光谱仪采集的可见/近红外光谱所建模型；相对最优PLSR模型的校正集和预测集相关系数分别为0.964、0.960,RMSEC和RMSEP分别为0.249和0.491,相对最优LS-SVM模型的校正集和预测集相关系数分别为0.966、0.960,RMSEC和RMSEP分别为0.249和0.444。为扩大品质指标含量的范围,对采集至猪肉不同部位肌肉的肉糜样本的品质组分定量分析进行了研究,比较了PLSR和LS-SVM两种定量校正方法对脂肪、蛋白质和水分含量的检测,对脂肪、蛋白质和水分含量的LS-SVM模型整体好于PLSR模型；对脂肪含量的分析基于傅立叶变换近红外光谱建立的模型优于基于便携式光谱仪采集光谱建立的模型,对蛋白质和水分含量的分析模型基于USB4000采集光谱建立的模型较优；综合分析脂肪、蛋白质和水分含量最优分析模型,脂肪和水分含量模型的校正及预测相关系数均高于0.9,模型的预测精度及稳定性均较好,但蛋白质含量模型校正及预测相关系数仅高于0.7,模型精度相对较差。
(2)研究了不同部位猪肉肉糜的定性判别分析。主要对4种不同肌肉类型的定性分析,比较了不同仪器,不同定性分析方法和不同光谱预处理所建鉴别模型的性能。不同光谱预测对模型性能产生的影响,结果表明原始光谱或一阶微分光谱所建模型的性能较优；不同光谱采集仪器的对比结果表明,便携式光谱仪获得的可见/近红外光谱更适用于不同肌肉类型猪肉肉糜的定性鉴别分析；比较不同的定性分析方法,DA方法所建模型性能略差,PLSDA最优模型校正及预测判别正确率分别为100%和94%,LA-SVMDA最优模型校正及预测判别正确率分别为99%和98%,表明可见/近红外光谱分析可快速鉴别不同部位猪肉肉糜。
(3)对猪肉肉糜脂肪酸含量进行了定量检测研究。利用可见/近红外光谱分析技术对猪肉背最长肌中14种脂肪酸含量的定量预测进行了分析,主要比较了不同便携式光谱仪获得的不同可见/近红外光谱建模、PLSR和LS-SVM两种不同建模方法及不同光谱预处理所建立的模型结果,两种不同的光谱仪和两种不同的定量分析方法对脂肪酸含量的检测没有显示出明显的优势,只是对某些指标的检测结果有相对较好的预测结果：比较C14：0、C16:0、C16:1、C17:0、C18:0、C18:1、C18:2、C18:3、C20:1、C20:4、C20:5、SFA、MUFA和PUFA的相对较优定量模型,其中C 17：0、C18：3和SFA模型相关性、模型精度及稳定性较优,C20：4、C20：5和MUFA模型性能性对略差,各指标检测精度需要进一步提高。
(4)对猪肉肉糜掺假检测进行了研究。采用傅立叶变换近红外光谱仪和便携式光谱仪USB4000获得同源掺假和非同源掺假两类样本不同等级掺假的可见/近红外光谱,建立不同等级掺假的定量分析模型。比较PLSR、PCR和MLR三种定量分析方法对不同等级掺假检测的建模结果,模型的总体性能差异不大,考虑到模型的稳定性、最优精度和适应性,比较不同波段、不同方法、不同掺假类型的模型性能,便携式光谱仪的检测结果较好,基于多元线性回归模型的性能较好,对同源掺假检测,基于可见/近红外光谱的MLR最优模型,校正预测及交互验证相关系数r分别为0.965、0.958和0.949,RMSEC、RMSEP和RMSECV分别为0.083、0.092和0.100；对非同源掺假检测,基于可见/近红外波段的MLR模型性能最优,校正预测及交互验证相关系数r分别为0.961、0.971和0.945,RMSEC、RMSEP和RMSECV分别为0.087、0.078和0.104,模型最稳定,预测精度最好,适用于猪肉肉糜掺假的快速检测。
(5)研究了猪肉新鲜度的定量评价方法。利用便携式光谱仪扫描采集保存不同时间猪肉的可见/近红外光谱,采用SMLR, PCR和PLSR三种定量分析方法建立新鲜度的预测模型,比较不同预处理对模型性能的影响,SMLR模型总体性能最差,PCR模型总体性能次之,PLSR模型的总体性能较优,3点光谱建立的PLSR模型结果最优,校正预测及交互验证相关系数r分别为0.970、0.956和0.887,RMSEC、RMSEP和RMSECV分别为2.63 mg/100g、3.45 mg/100g和5.01mg/100g。研究结果表明,可见/近红外光谱分析技术能实现对猪肉新鲜度的快速检测,对实现监测猪肉品质安全提供了保障和依据。
Meat and meat products are one of the essential sources of human nutritional component, such as protein, vitamins and minerals. Pork is one of the main components of human diet, which is the largest production and consumption in China. Along with improvement of living standard and changing of meal structure, the demand for pork quality and safety should be higher than before. China is a major pork producer as well as a major pork consumer, but not powerful in international market. Testing technology and assessment method have complicated formalities, which reduce the efficiency of regulatory authority. Therefore, rapid detection and evaluation of pork quality and safety become one of important part in the development of pork industry and the assurance of food safety.
The research object is fresh minced pork. Detection of pork quality and safety were carried out based on visible/near infrared (NIR) spectroscopy, chemometrics techniques combined with modern physical-chemical analysis techniques. Quantitative models were established based on visible/NIR spectra for fat, intramuscular fat, protein, moisture and fatty acids determination, and qualitative models were also established for muscle discriminant from different part of pig. In this dissertation, detection of adulteration and freshness using visible/NIR spectra were also studied for safe quality detection of pork.
The main results and conclusions were:
(1) Quantitative analysis of minced pork quality was studied. Intramuscular fat, protein, moisture content in minced pork form longissimus dorsi muscle were analyzed quantitatively based on visible/NIR spectra by a portable instrument. Comparison results of different calibration methods of stepwise multi linear regression (SMLR), partial least squares regression (PLSR), least squares support vector machines (LS-SVM) and different spectra pretreatments showed that the performance of PLSR and LS-SVM model was much better than SMLR model. The best model of PLSR was established based on the raw spectra, and the best models of LS-SVM was based on the spectra with orthogonal signals correction (OSC) pretreatment for intramuscular fat, protein and moisture content, which was closed to PLSR calibration performance. Further research for quantitative analysis of Intramuscular fat was studied. Comparison results of different calibration methods of PLSR, LS-SVM and different spectra pretreatments and different detectors showed that the performance of model based on the spectra acquired by InGaAs detector was much better. The performance of LS-SVM model was close to that of PLSR model, but the performance of PLSR model based on the spectra acquired by USB4000 was much better than the LS-SVM model. For the best PLSR model, the correlation coefficients of calibration and validation were 0.964 and 0.960, respectively; the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.249and 0.491, respectively. For the best LS-SVM model, the correlation coefficients of calibration and validation were 0.966 and 0.960, respectively; RMSEC and RMSEP were 0.249 and 0.444, respectively. In order to expanding the range of the content for quality index, the samples of minced pork collected from different muscle of pig were analyzed quantitatively. Comparison results of different calibration methods of PLSR, LS-SVM and different spectra pretreatments and different detectors showed that the performance of LS-SVM model was better than PLSR model for fat, protein and moisture content. For fat, the model based on the spectra acquired by InGaAs detector performed better. For protein and moisture, the model based on the spectra acquired by USB4000 performed better. The correlation coefficients for calibration and validation were above 0.9 for detection of fat and moisture, and the precision and stability were strong. But for the protein, the correlation coefficient for calibration and validation were above 0.7, the precision was relatively worse.
(2) Qualitative analysis of minced pork from 4 different muscle was studied. Comparison results of different calibration methods of discriminant analysis (DA), partial least squares discriminant analysis (PLSDA), least squares support vector machines discriminant analysis (LS-SVMDA) and different spectra pretreatments and different detectors showed that the model based on the raw spectra or first derivative performed better. Visible/NIR spectra obtained by portable instrument were more suitable for discrimination of 4 different muscles. The discriminant results of DA model were a bit worse than PLSDA model and LA-SVMDA model. The discriminant accuracy of calibration and validation for the best PLSDA model were 100% and 94%, respectively and for LA-SVMDA were 99% and 98%, respectively.
(3) Quantitative analysis of fatty acids was researched. Comparison results of different calibration methods of PLSR, LS-SVM and different spectra pretreatments and different detectors for detection of fatty acid content showed that there were no significant difference between PLSR and LS-SVM or two different detectors as a whole. The correlations, precision and stability were relatively better for C17:0, C18:3 and SFA, but worse for C20:4, C20:5 and MUFA. The precision of quantitative analysis for fatty acids should be improved.
(4) Quantitative analysis of adulteration in minced pork were studied with different grade adulteration based on different detectors. Comparison results of different calibration methods and different wavebands and different type adulteration showed that the performance of multi linear regression (MLR) model based on visible/NIR spectra was much better, the correlation coefficients of calibration, validation and cross validation were 0.965,0.958 and 0.949, respectively; RMSEC, RMSEP and RMSECV were 0.083,0.092 and 0.100, respectively for adulterated with the same homologous material, and the correlation coefficients of calibration, validation and cross validation were 0.961,0.971 and 0.945, respectively; RMSEC, RMSEP and RMSECV were 0.087,0.078 and 0.104, respectively for adulterated with the nonhomologous material.
(5) Quantitive analysis of freshness of pork were studied. The models established using SMLR, PCR and PLSR based on the visible NIR spectra. Comparison results of different calibration methods and different spectra pretreatment showed that the performance of PLSR model was much better and the SMLR model was much worse. The best model using first derivative spectra achieved the correlation coefficients of calibration, validation and cross validation were 0.970,0.956 and 0.887, respectively; RMSEC, RMSEP and RMSECV were 2.63 mg/100g,3.45 mg/100g and 5.01 mg/100g, respectively. This study demonstrated that visible/NIR spectroscopy can be successfully applied as a rapid method to determine the quality and safety of pork.
引文
[1]刘春芳,王济民.中国生猪产业发展现状与展望[J].农业展望,2010,3,28-31.
[2]邓蓉,王伟.论我国猪肉价格的形成及稳定猪肉价格的对策[J].现代化农业,2007,12,23-26.
[3]崔超,吴林海,李冰冰.中国猪肉供应现状及产业发展前景[J].安徽农业科学,2008,22,9550-9551.
[4]中华人民共和国国家统计局网站,年度数据,http://www.stats.gov.cn/tjsj/ndsj.
[5]杨小川,陈娴试论我国猪肉产业国际竞争力问题[J].农村经济,2008,5,57-60.
[6]陈小静,涂江华.我国猪肉产业及其贸易特征分析[J].中国市场,2008,44,18-19.
[7]刘合光,孙东升.中国生猪产业三大发展趋势与猪肉消费展望[J].中国畜牧杂志,2010,6,12-14,27.
[8]房慧,荣倩.我国猪肉产业面临的挑战[J].食品安全导刊,2010,9.20-27.
[9]屠康.肉类品质无损检测技术的研究进展[J].西北农林科技大学学报(自然科学版),2005,1,25-28.
[10]孔宪琴,黄素珍.肉品品质的无损检测方法[J].肉类研究,2008,8,66-69.
[11]张小宇.肉类无损检测新技术[J].肉类研究,2006,3,1-3.
[12]李静,操庆国.无损检测技术在农产品品质检验中的应用[J].中国科技信息,2010,21,79-80.
[13]潘立刚,张缙,陆安祥.马智宏,韩平.农产品质量无损检测技术研究进展与应用[J].农业工程学报,2008.S2,325-330.
[14]刘静,章程辉,黄勇平.无损检测技术在农产品品质评价中的应用[J].福建热作科技,2007,3,32-35.
[15]李敏,李宪华,奚星林,潘丙珍,庞世琦.无损检测技术在食品分析中的应用[J].检验检疫科学,2008,6,60-62.
[16]卜凡艳,韩剑众.无损检测技术在食品品质检测中的应用[J].食品工业科技,2007,7,221-224.
[17]孙通,徐惠荣,应义斌.近红外光谱分析技术在农产品/食品品质在线无损检测中的应用研究进展[J].光谱学与光谱分析,2009,1,122-126.
[18]刘燕德,刘涛,孙旭东,欧阳爱国,郝勇.拉曼光谱技术在食品质量安全检测中的应用[J].光谱学与光谱分析,2010,11,3007-3012.
[19]张涛,李军科,刘柱,袁士芳.食品科学中拉曼光谱研究进展[J].安徽农业科学,2007,33, 10563-10565.
[20]闫战科,张宏建,应义斌.THz技术在农产品/食品品质检测中的应用[J].光谱学与光谱分析,2007,11,2228-2234.
[21]刘芳,赵峰.超声波技术在食品生产检测和食品安全检测中的应用进展[J].福建分析测试,2008,4,27-31.
[22]周莉.超声波在食品工业中的应用的研究现状[J].肉类研究,2009,2,67-71.
[23]韩平,潘立刚,马智宏,陆安祥,王纪华.X射线无损检测技术在农产品品质评价中的应用[J].农机化研究,2009,10,6-10.
[24]田有文,王晓娟.基于高光谱图像技术的农产品品质无损检测[J].农机化研究,2009,10,220-226.
[25]王雷,乔晓艳,董有尔,张姝,尚艳飞.高光谱图像技术在农产品检测中的应用进展[J].应用光学,2009,4,639-645.
[26]童钊,廖桂平,李锦卫,金晶.机器视觉技术在农产品检测中的应用研究[J].农业网络信息,2008,11,18-21.
[27]应义斌,饶秀勤,赵匀,蒋亦元.机器视觉技术在农产品品质自动识别中的应用研究进展[J].农业工程学报,2000,3,4-8.
[28]周凝,刘宝林,王欣.核磁共振技术在食品分析检测中的应用[J].食品工业科技,2011,1,325-329.
[29]陈卫江,林向阳,阮榕生,何承云,朱榕璧,刘玉环.核磁共振技术无损快速评价食品水分的研究[J].食品研究与开发,2006,4,125-127.
[30]张哲,佟金.电子鼻和电子舌在食品检测中的研究和应用[J].华中农业大学学报,2005,S1,25-30.
[31]王俊,胡桂仙,于勇,周亦斌.电子鼻与电子舌在食品检测中的应用研究进展[J].农业工程学报,2004,2,292-295.
[32]吴莉莉,李辉,惠国华,袁超.传感器技术在农产品无损检测中的应用研究[J].江西农业学报,2010,5,]01-105.
[33]彭彦昆,张雷蕾.光谱技术在生鲜肉品质安全快速检测的研究进展[J].食品安全质量检测学报,2010,2,62-72.
[34]吴建虎,彭彦昆,陈菁菁,王伟,高晓东,黄慧.基于高光谱散射特征的牛肉品质参数的预测研究[J].光谱学与光谱分析,2010,7,1815-1819.
[35]吴建虎,彭彦昆,高晓东,陈菁菁,黄慧.基于VIS/NIR高光谱散射特征预测牛肉的嫩度[J].食品 安全质量检测技术,2009,1,20-26.
[36]吴建虎,彭彦昆,江发潮,王伟,李永玉,高晓东.牛肉嫩度的高光谱法检测技术[J].农业机械学报,2009,12,135-138,150.
[37]高晓东,吴建虎,彭彦昆,陈菁菁,陶斐斐.基于高光谱成像技术的牛肉大理石花纹的评估[J].农产品加工(学刊),2009,10,33-37.
[38]刘木华,蔡健荣,周小梅.x射线图像在农畜产品内部品质无损检测中的应用[J].农机化研究,2004,2,193-196.
[39]张迎雪.荧光检验能快速检测肉制品中的大肠杆菌[J].山东食品科技,2003,4,36-37.
[40]钟赛意,王善荣,刘寿春.超声波技术在动物性食品中的应用[J].肉类研究,2006,11,33-37.
[41]麻建国,周建军.超声波技术在食品检测中的应用[J].食品与发酵工业,1998,5,52-58.
[42]李正英,陈锦屏,陈忠军,杨俊峰.羊肉超声波嫩化技术的研究[J].食品科学,2006,12,413-416.
[43]庄玉亭,赵月兰.肉品新鲜度检测方法[J].河北科技大学学报.1999,2,63-65.
[44]陈纯.计算机图像处理技术与算法[M].北京：清华大学出版社,2003,7.
[45]李明静,刘贯勇,张志伟.计算机视觉在牛肉自动分级技术中的应用[J].肉类研究,2007,6,18-20.
[46]张哲,王楚端,王立贤,李俊奇,周伟良,李振宽.计算机视觉技术在猪眼肌肌内脂肪含量测定中的应用[J].猪业科学,2006,2,24-25.
[47]陆婉珍.现代近红外光谱分析技术(第二版).北京：中国石化出版社,2007,1.
[48]严衍禄.近红外光谱分析基础与应用.北京：中国轻工业出版社,2005.1.
[49]李民赞,韩东海,王秀.光谱分析技术及其应用.北京：科学出版社,2006,8.
[50]李炎,张月学,徐香玲,蒿若超.近红外反射光谱(NIRS)分析技术及其在农业上的应用[J].黑龙江农业科学,2008,1,105-108.
[51]阮治纲,李彬.近红外光谱分析技术的原理及在中药材中的应用[J].药物分析杂志,2011,2,408-417.
[52]高荣强,范世福.现代近红外光谱分析技术的原理及应用[J].分析仪器,2002,3,9-12.
[53]Prevolnik, M., Candek-Potokar, M., Skorjanc, D. Ability of NIR spectroscopy to predict meat chemical composition and quality-a review[J]. Czech Journal of Animal Science-UZPI,2004,49,500-510.
[54]Prieto, N., Roehe, R., Lavin, P., Batten, G., Andres, S. Application of near infrared reflectance spectroscopy to predict meat and meat products quality:A review[J]. Meat Science,2009,83,175-186.
[55]Rudbotten, R., Nilsen, B. N., Hildrum, K. I. Prediction of beef quality attributes from early post mortem near infrared reflectance spectra[J]. Food Chemistry,2000,69,427-436.
[56]Rudbotten, R., Mevik, B. H., Hildrum, K. I. Prediction and classification of tenderness in beef from non-invasive diode array detected NIR spectra[J]. Journal of near Infrared Spectroscopy 2001,9, 199-210.
[57]Park, B., Chen, Y. R., Hruschka, W. R., Shackelford, S. D., Koohmaraie, M. Principal component regression of near-infrared reflectance spectra for beef tenderness prediction[J]. Transactions of the Asae,2001,44,609-615.
[58]Jeyamkondan, S., Kranzler, G., Morgan, B., Rust, S. Predicting beef tenderness using near-infrared spectroscopy[J]. Monitoring Food Safety, Agriculture, and Plant Health,2004,5271,356-365.
[59]Rust, S. R., Price, D. M., Subbiah, J., Kranzler, G., et al. Predicting beef tenderness using near-infrared spectroscopy [J]. Journal of Animal Science,2008,86,211-219.
[60]Venel, C., Mullen, A. M., Downey, G., Troy, D. J. Prediction of tenderness and other quality attributes of beef by near infrared reflectance spectroscopy between 750 and 1100 nm; further studies[J]. Journal of near Infrared Spectroscopy,2001,9,185-198.
[61]Cozzolino, D., De Mattos, D., Martins, D. V. Visible/near infrared reflectance spectroscopy for predicting composition and tracing system of production of beef muscle[J]. Animal Science,2002,74. 477-484.
[62]Cozzolino, D., Martins, V., Murray, I. Visible and near infrared spectroscopy of beef longissimus dorsi muscle as a means of dicriminating between pasture and com silage feeding regimes[J]. Journal of near Infrared Spectroscopy,2002,10,187-193.
[63]Alomar, D., Gallo, C., Castaneda, M., Fuchslocher, R. Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS) [J]. Meat Science,2003,63,441-450.
[64]Togersen, G., Arnesen, J., Nilsen, B. N., Hildrum, K. I. On-line prediction of chemical composition of semi-frozen ground beef by non-invasive NIR spectroscopy[J]. Meat Science,2003,63,515-523.
[65]Liu, Y. L., Lyon, B. G., Windham, W. R., Realini, C. E., et al. Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study[J]. Meat Science,2003,65,1107-1115.
[66]Leroy, B., Lambotte, S., Dotreppe, O., Lecocq, H., et al. Prediction of technological and organoleptic properties of beef Longissimus thoracis from near-infrared reflectance and transmission spectra[J]. Meat Science,2004,66,45-54.
[67]Hildrum, K. I., Nilsen, B. N., Westad, F., Wahlgren, N. M. In-line analysis of ground beef using a diode array near infrared instrument on a conveyor belt[J]. Journal of near Infrared Spectroscopy,2004, 12,367-376.
[68]Prevolnik, M., Candek-Potokar, M., Skorjanc, D., Velikonja-Bolta, S., et al. Predicting intramuscular fat content in pork and beef by near infrared spectroscopy[J]. Journal of near Infrared Spectroscopy, 2005,13,77-85.
[69]Gangidi, R. R., Proctor, A., Pohlman, F. W., Meullenet, J. F. Rapid determination of spinal cord content in ground beef by near-infrared spectroscopy[J]. Journal of Food Science,2005,70,397-400.
[70]Prieto, N., Andres, S., Giraldez, F. J., Mantecon, A. R., Lavin, P. Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples[J]. Meat Science,2006,74,487-496.
[71]Bae, Y. M, Cho, S. I., Kim, Y. Y., Park,I. S., Hwang, K. Y. Estimation of freshness of beef using near-infrared spectroscopy [J]. Transactions of the Asabe.2006,49,557-561.
[72]De Marchi. M., Berzaghi, P., Boukha, A., Mirisola. M., Gallo, L. Use of near infrared spectroscopy for assessment of beef quality traits[J]. Italian Journal of Animal Science,2007,6,421-423.
[73]Prieto, N., Andres, S., Giraldez, F. J., Mantecon, A., Lavin, P. Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples[J]. Meat Science,2008,79,692-699.
[74]Prieto, N., Andres, S., Giraldez. F. J., Mantecon, A. R., Lavin. P. Discrimination of adult steers (oxen) and young cattle ground meat samples by near infrared reflectance spectroscopy (NIRS) [J]. Meat Science,2008,79,198-201.
[75]Ripoll, G., Alberti, P., Panea, B., Olleta, J. L., Sanudo, C. Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef[J]. Meat Science,2008,80,697-702.
[76]Andres, S., Silva, A., Soares-Pereira, A. L., Martins, C., et al. The use of visible and near infrared reflectance spectroscopy to predict beef M-longissimus thoracic et lumborum quality attributes[J]. Meat Science,2008,78,217-224.
[77]Rosenvold, K., Micklander, E., Hansen, P. W., Burling-Claridge, R., et al. Temporal, biochemical and structural factors that influence beef quality measurement using near infrared spectroscopy[J]. Meat Science,2009,82,379-388.
[78]Sierra, V., Aldai, N., Castro, P., Osoro, K., et al. Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy[J]. Meat Science,2008,78,248-255.
[79]Prieto, N., Ross, D. W., Navajas, E. A., Richardson, R.I., et al. Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy[J]. Animal,2011,5,155-165.
[80]Prieto, N., Ross, D. W., Navajas, E. A., Nute, G. R., et al. On-line application of visible and near infrared reflectance spectroscopy to predict chemical-physical and sensory characteristics of beef quality[J]. Meat Science,2009,83,96-103.
[81]Chan, D. E., Walker, P. N., Mills, E. W. Prediction of pork quality characteristics using visible and near-infrared spectroscopy[J]. Transactions of the Asae,2002,45,1519-1527.
[82]Gonzalez-Martin,I., Gonzalez-Perez, C., Hernandez-Mendez, J., Alvarez-Garcia, N., Andaluz, J. L. H. On-line non-destructive determination of proteins and infiltrated fat in Iberian pork loin by near infrared spectrometry with a remote reflectance fibre optic probe[J]. Analytica Chimica Acta,2002,453, 281-288.
[83]Gonzalez-Martin,I., Gonzalez-Perez, C., Hernandez-Mendez, J., Alvarez-Garcia. N. Mineral analysis (Fe, Zn, Ca, Na, K) of fresh Iberian pork loin by near infrared reflectance spectrometry-Determination of Fe, Na and K with a remote fibre-optic reflectance probe[J]. Analytica Chimica Acta,2002,468. 293-301.
[84]Cozzolino, D., Barlocco, N., Vadell, A., Ballesteros, F., Gallieta, G. The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenised pork muscle[J]. Lebensmittel-Wissenschaft Und-Technologie-Food Science and Technology,2003,36. 195-202.
[85]Geesink, G. H., Schreutelkamp, F. H., Frankhuizen, R., Vedder, H. W., et al. Prediction of pork quality attributes from near infrared reflectance spectra[J]. Meat Science,2003,65,661-668.
[86]Cozzolino, D., Murray,I. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy [J]. Lebensm.-Wiss. Technol.-Food Sci. Technol,2004,37,447-452.
[87]Hoving-Bolink, A., Vedder, H., Merks, J., De Klein, W., et al. Perspective of NIRS measurements early post mortem for prediction of pork quality[J]. Meat Science,2005,69,417-423.
[88]Savenije, B., Geesink, G. H., van der Palen, J. G. P., Hemke, G. Prediction of pork quality using
visible/near-infrared reflectance spectroscopy[J]. Meat Science,2006,73,181-184.
[89]Barlocco, N., Vadell, A., Ballesteros, F., Galietta, G., Cozzolino, D. Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy[J]. Animal Science,2006,82,111-116.
[90]Xing, J., Ngadi, M., Gunenc, A., Prasher, S., Gariepy, C. Use of visible spectroscopy for quality classification of intact pork meat[J]. Journal of Food Engineering,2007,82,135-141.
[91]Prevolnik, M., Candek-Potokar, M., Novic, M., Skorjanc, D. An attempt to predict pork drip loss from pH and colour measurements or near infrared spectra using artificial neural networks[J]. Meat Science, 2009,83,405-411.
[92]del Moral, F. G., Guillen, A., del Moral, L. G., O'Valle, F., et al. Duroc and Iberian pork neural network classification by visible and near infrared reflectance spectroscopy[J]. Journal of Food Engineering,2009,90,540-547.
[93]Prevolnik, M., Candek-Potokar, M., Skorjanc, D. Predicting pork water-holding capacity with NIR spectroscopy in relation to different reference methods [J]. Journal of Food Engineering.2010.98, 347-352.
[94]Monroy. M., Prasher, S., Ngadi, M. O., Karimi, Y. Pork meat quality classification using Visible/Near-Infrared spectroscopic data[J]. Biosystems Engineering,2010,107,271-276.
[95]Guillen, A., del Moral, F. G., Herrera, L. J., Rubio, G., et al. Using near-infrared spectroscopy in the classification of white and iberian pork with neural networks[J]. Neural Computing and Applications, 2010,19,465-470.
[96]Chou, C. H., Chyr, C. Y. L., Chen, R. L. C., Shaw, J. T. Evaluation of Freshness of Ground Pork Tenderloin by Visible/Near Infrared Spectroscopy [J]. Journal of Food and Drug Analysis,2010,18, 107-112.
[97]Fumiere.O., Sinnaeve, G., Dardenne, P. Attempted authentication of cut pieces of chicken meat from certified production using near infrared spectroscopy[J]. Journal of near Infrared Spectroscopy,2000,8, 27-34.
[98]Abeni, F., Bergoglio, G. Characterization of different strains of broiler chicken by carcass measurements, chemical and physical parameters and NIRS on breast muscle[J]. Meat Science,2001, 57,133-137.
[99]Cozzolino, D., Murray, I. Effect of sample presentation and animal muscle species on the analysis of meat by near infrared reflectance spectroscopy[J]. Journal of near Infrared Spectroscopy,2002,10, 37-44.
[100]Windham, W. R., Lawrence, K. C., Feldner, P. W. Prediction of fat content in poultry meat by near-infrared transmission analysis[J]. Journal of Applied Poultry Research,2003,12,69-73.
[101]Meullenet, J. F., Jonville, E., Grezes, D., Owens, C. M. Prediction of the texture of cooked poultry pectoralis major muscles by near-infrared reflectance analysis of raw meat[J]. Journal of Texture Studies,2004,35,573-585.
[102]Berzaghi, P., Dalla Zotte, A., Jansson, L. M., Andrighetto, I. Near-infrared reflectance spectroscopy as a method to predict chemical composition of breast meat and discriminate between different n-3 feeding sources[J]. Poultry Science,2005,84,128-136.
[103]Viljoen, M., Hoffman, L. C., Brand, T. S. Prediction of the chemical composition of freeze dried ostrich meat with near infrared reflectance spectroscopy [J]. Meat Science,2005,69,255-261.
[104]Tejerina, D., Lopez-Parra, M. M., Garcia-Torres, S. Potential used of near infrared reflectance spectroscopy to predict meat physico-chemical composition of guinea fowl (Numida meleagris) reared under different production systems[J]. Food Chemistry,2009,113,1290-1296.
[105]Samuel, D., Park, B., Sohn, M., Wicker, L. Visible-near-infrared spectroscopy to predict water-holding capacity in normal and pale broiler breast meat[J]. Poultry Science,2011,90,914-921.
[106]Cozzolino, D., Murray,I., Scaife, J., Paterson, R. Study of dissected lamb muscles by visible and near infrared reflectance spectroscopy for composition assessment[J]. Animal Science,2000,70,417-423.
[107]McGlone, V. A., Devine, C. E., Wells, R. W. Detection of tenderness, post-rigor age and water status changes in sheep meat using near infrared spectroscopy [J]. Journal of near Infrared Spectroscopy, 2005,13,277-285.
[108]Andres, S., Murray, I., Navajas, E. A., Fisher, A. V., et al. Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy[J]. Meat Science,2007,76,509-516.
[109]Viljoen, M., Hoffman, L., Brand, T. Prediction of the chemical composition of mutton with near infrared reflectance spectroscopy[J]. Small Ruminant Research,2007,69,88-94.
[110]Gonzalez-Redondo, P., Gomez, L. V., Herrero, L. G., Fernandez-Cabanas, V. M. Chemical composition of wild rabbit meat (Oryctolagus cuniculus) and viability of its prediction by near infrared spectroscopy[J]. Itea-Informacion Tecnica Economica Agraria,2010,106,184-196.
[111]Guy, F., Prache, S., Thomas, A., Bauchart, D., Andueza, D. Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS) [J]. Food Chemistry,2011,127, 1280-1286.
[112]Ding, H. B., Xu, R. J. Near-infrared spectroscopic technique for detection of beef hamburger adulteration[J]. Journal of Agricultural and Food Chemistry,2000,48,2193-2198.
[113]Kang, J. O., Park, J. Y., Choy, Y. H. Effect of grinding on color and chemical composition of pork sausages by near infrared spectrophotometric analyses[J]. Asian-Australasian Journal of Animal Sciences,2001,14,858-861.
[114]Ortiz-Somovilla, V., Espana-Espana, F., De Pedro-Sanz, E., Gaitan-Jurado, A. Meat mixture detection in Iberian pork sausages[J]. Meat Science,2005,71,490-497.
[115]Ortiz-Somovilla, V., Espa a-Espa a, F., Gaitan-Jurado, A., Perez-Aparicio, J., De Pedro-Sanz, E. Proximate analysis of homogenized and minced mass of pork sausages by NIRS[J]. Food Chemistry, 2007,101,1031-1040.
[116]Garcia-Rey, R.. Garcia-Olmo. J., De Pedro. E., Quiles-Zafra, R., Luque de Castro, M. Prediction of texture and colour of dry-cured ham by visible and near infrared spectroscopy using a fiber optic probe[J]. Meat Science,2005,70,357-363.
[117]Gaitan-Jurado, A., Ortiz-Somovilla, V., Espa a-Espa a, F., Perez-Aparicio, J., De Pedro-Sanz, E. Quantitative analysis of pork dry-cured sausages to quality control by NIR spectroscopy [J]. Meat Science,2008,78,391-399.
[118]Gonzalez-Martin, M.I., Bermejo, C. F., Hierro, J. M. H., Gonzalez, C. I. S. Determination of hydroxyproline in cured pork sausages and dry cured beef products by NIRS technology employing a fibre-optic probe[J]. Food Control 2009,20,752-755.
[119]ColleIl, C., Gou, P., Picouet, P., Arnau, J., Comaposada, J. Feasibility of near-infrared spectroscopy to predict a(w) and moisture and NaCl contents of fermented pork sausages[J]. Meat Science,2010,85, 325-330.
[120]于红樱,祁慧娜,臧勇军.近红外分析技术在肉与肉制品分析中的应用[J].肉类工业,2006,11,8-9.
[121]韩晨,贺稚非.肉品检测技术的研究进展[J].肉类研究,2006,12,21-23,32.
[122]王梅.肉制品检测技术的应用[J].肉类研究,2009,7,50-53.
[123]蒋耀庭,梁承红,陈菊娜.现代近红外光谱分析在食品检测中的应用[J].粮食与食品工业,2010,1,51-54.
[124]熊成,董庆利,曾静,胡孟晗.近红外光谱分析技术在肉品品质检测中的应用[J].食品与发酵工业,2010,12,141-145.
[125]成芳,廖宜涛,马君伟.猪肉品质无损检测研究进展[J].浙江大学学报(农业与生命科学版),2010,2,199-206.
[126]徐霞,成芳,应义斌.近红外光谱技术在肉品检测中的应用和研究进展[J].光谱学与光谱分析,2009,7,1876-1880.
[127]刘炜,俞湘麟,孙东东,陈鲁勇,刘全.傅立叶变换近红外光谱法快速检测鲜猪肉中肌内脂肪、蛋白质和水分含量[J].养猪,2005,3,47-50.
[128]姬瑞勤,王忠义,黄岚,侯瑞锋,徐志龙.鲜肉深层水分的无损检测方法研究[J].现代科学仪器,2006,1,119-121.
[129]姬瑞勤,黄岚,刘莉,王忠义.鲜肉水分近红外漫反射方法及实验研究[J].光谱学与光谱分析,2008,(8).1767-1771
[130]刘魁武,成芳,林宏建,孙通,许凯,胡雷秀,应义斌,徐惠荣.可见/近红外光谱检测冷鲜猪肉中的脂肪、蛋白质和水分含量[J].光谱学与光谱分析,2009,1,102-105.
[131]廖宜涛,樊玉霞,伍学千,成芳.猪肉肌内脂肪含量的可见/近红外光谱在线检测[J].农业机械学报,2010,9,681-684.
[132]Liao, Y.T., Fan, Y.X., Cheng, F. On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy[J]. Meat Science,2010,86,901-907.
[133]孟宪江,张铁强,白英奎,申铉国.利用BP神经网络光谱分类法研究肉品新鲜度[J].光谱实验室,2004,5,970-973.
[134]侯瑞锋,黄岚,王忠义,丁海曙,徐志龙.用近红外漫反射光谱检测肉品新鲜度的初步研究[J].光谱学与光谱分析,2006,12,2193-2196.
[135]赵杰文,翟剑妹,刘木华,蔡健荣.牛肉嫩度的近红外光谱法检测技术研究[J].光谱学与光谱分 析,2006,4,640-642.
[136]张德权,陈宵娜,孙素琴,李春红,张柏林,李勇,李淑荣,李庆鹏,周洪杰.羊肉嫩度傅里叶变换近红外光谱偏最小二乘法定量分析研究[J].光谱学与光谱分析,2008,11,2550-2553.
[137]杨勇,任健,王存堂,王文侠,康晶.鹅肉嫩度的测定及感官评价[J].河南工业大学学报(自然科学版),2009,6,29-32,87.
[138]张根伟,文星,王忠义,赵东杰,黄岚.基于稳态空间分辨光谱技术的猪肉嫩度测量方法研究[J].光谱学与光谱分析,2010,10,2793-2796.
[139]胡耀华,熊来怡,蒋国振,刘聪,郭康权,佐竹隆显.基于可见光和近红外光谱鲜猪肉蒸煮损失和嫩度检测的研究[J].光谱学与光谱分析,2010,11,2950-2953.
[]40]Cai, J., Chen, Q., Wan, X., Zhao, J. Determination of total volatile basic nitrogen (TVB-N) content and Warner-Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy[J]. Food Chemistry,2009,126,1354-1360.
[141]文星,梁志宏,张根伟,黄岚,王忠义.基于稳态空间分辨光谱的猪肉新鲜度检测方法[J].农业工程学报,2010,9,334-339.
[142]杨红菊,姜艳彬,候东军,赵丽丽.注胶肉的近红外光谱快速判别分析[J].肉类研究,2008,11,62-64.
[143]张宁,张德权,李淑荣,李庆鹏.近红外光谱结合SIMCA法溯源羊肉产地的初步研[J].农业工程学报,2008,12,309-312.
[144]胡耀华.郭康权,铃木孝范,野口刚,胡西红,佐竹隆显.基于可见光和近红外光谱的肉色检测[J].西北农林科技大学学报(自然科学版),2008,7,160-164.
[145]胡耀华,郭康权,野口刚,河野澄夫,佐竹隆显.基于近红外光谱检测猪肉系水力的研究[J].光谱学与光谱分析,2009,12,3259-3262.
[146]吴金亮,高新,殷云飞,陈吉红,杨国明.云南地方猪种肉质NIR指纹主成分分析和聚类分析研究[J].中国畜牧兽医,2010,11,94-99.
[147]杨志敏,丁武.原料肉中掺大豆蛋白的近红外检测技术的研究[J].肉类工业,2010,10,29-33.
[148]杨建松,孟庆翔,任丽萍,周振明,解祥学.近红外光谱法快速评定牛肉品质[J].光谱学与光谱分析,2010,3,685-687.
[149]廖宜涛,樊玉霞,伍学千,成芳.猪肉pH值的可见近红外光谱在线检测研究[J].光谱学与光谱分析,2010,3,681-684.
[150]朱迅涛.近红外分析技术在火腿肠检测中的应用[J].肉类研究,2002,2,42-44.
[151]赵丽丽,张录达,宋忠祥,李勇,严衍禄,马长伟.近红外光谱定量检测腌腊肉制品品质的研究[J].光谱学与光谱分析,2007,1,4649.
[152]Murray, A. C. How Much Fat Should Pig Muscle Contain? Advances in Pork Production13, Abstract 2002,34.
[153]Savitzky, A., Golay, M. J. E. Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry[J].1964,36,1627-1639.
[154]谢军,潘涛,陈洁梅,陈华舟,任小焕.血糖近红外光谱分析的Savitzky-Golay平滑模式与偏最小二乘法因子数的联合优选[J].分析化学,2010,3,342-346.
[155]芦永军,曲艳玲,宋敏.近红外相关光谱的多元散射校正处理研究[J].光谱学与光谱分析,2007,5,877-880.
[156]于海燕.黄酒品质和酒龄的近红外光谱分析方法研究.浙江大学博士学位论文.2007,1.
[157]Wold, S., Antti, H., Lindgren, F., hman, J. Orthogonal signal correction of near-infrared spectra[J]. Chemometrics and Intelligent Laboratory Systems,1998,44,175-185.
[158]张海东,赵杰文,刘木华.基于正交信号校正和偏最小二乘(OSC/PLS)的苹果糖度近红外检测[J].食品科学,2005,6,189-192.
[159]刘广军,戴冬梅,高洪涛.正交信号校正算法及其在光谱处理中的应用[J].山东建筑工程学院学报,2005,2,83-86,91.
[160]陆辉山.水果内部品质可见/近红外光谱实时无损检测关键技术研究.浙江大学博士学位论文.2006,8.
[161]单杨,朱向荣,许青松,梁逸曾.近红外光谱结合小波变换-径向基神经网络用于奶粉蛋白质与脂肪含量的测定[J].红外与毫米波学报,2010,2,128-131.
[162]夏俊芳,李小昱.脐橙VC含量近红外光谱消噪小波优化[J].农业机械学报,2009,4,143-146.
[163]Liang, L., Wang, B., Guo, Y., Ni, H., Ren, Y. A support vector machine-based analysis method with wavelet denoised near-infrared spectroscopy[J]. Vibrational Spectroscopy,2009,49,274-277.
[164]许禄,邵学广.化学计量学方法(第二版).北京：科学出版社.2004,90-175.
[165]金航峰,黄凌霞,吴迪,金佩华,楼程富.用最小二乘支持向量机的可见-近红外光谱测定蜂花粉贮存时间[J].红外与毫米波学报,2010,3,216-219.
[166]王义峰,张中卫.LSSVM在酒类近红外光谱检测中的应用[J].信息技术,2009,11,90-92.
[167]李晓丽,胡兴越,何勇.基于主成分和多类判别分析的可见-红外光谱水蜜桃品种鉴别新方法[J].红外与毫米波学报,2006,6,417-420.
[168]傅霞萍.水果内部品质可见/近红外光谱无损检测方法的实验研究.浙江大学博士学位论文.2008.4.
[169]谢丽娟.转基因番茄的可见/近红外光谱快速无损检测方法.浙江大学博士学位论文.2009.2.
[170]张晓曼.基于SVM的近红外光谱定性分析及其应用.浙江大学硕士学位论文.2008.6.
[171]葛红艳,邢光龙,王伟超.基于支持向量机分类的回归方法[J].电子技术,2008,9,44-46.
[172]Swatland, H. Infrared fiber optic spectrophotometry of meat[J]. Journal of Animal Science,1983,56, 1329-1333.
[173]Swatland, H. Color measurements on pork and veal carcasses by fiber optic spectrophotometry[J]. Canadian Institute of Food Science and Technology Journal,1986,19,170-173.
[174]Swatland, H. Post-mortem spectrophotometry of pork and beef using quartz optical fibres[J]. Meat Science,1986,17,97-106.
[175]Murray, I. The NIR spectra of homologous series of organic compounds[J]. In Proceedings of the International NIR/NIT Conference; Hollo, J., Kaffka, K. J., Gonczy, J. L., Eds.; Akademiai Kiado:Budapest, Hungary,1986; pp 13-28.
[176]Williams. P., Norris. K. Near-infrared technology in the agricultural and food industries, American Association of Cereal Chemists. Inc[M],1987.17-34.
[177]Shenk, J., Workman, J., Westerhaus, M. Application of NIR spectroscopy to agricultural products[J]. Practical Spectroscopy Series,1992,13,383-431.
[178]Nanke, K., Sebranek, J., Olson, D. Color characteristics of irradiated vacuum packaged pork, beef, and turkey[J]. Journal of Food Science-Chicago,1998,63,1001-1006.
[2]邓蓉,王伟.论我国猪肉价格的形成及稳定猪肉价格的对策[J].现代化农业,2007,12,23-26.
[3]崔超,吴林海,李冰冰.中国猪肉供应现状及产业发展前景[J].安徽农业科学,2008,22,9550-9551.
[4]中华人民共和国国家统计局网站,年度数据,http://www.stats.gov.cn/tjsj/ndsj.
[5]杨小川,陈娴试论我国猪肉产业国际竞争力问题[J].农村经济,2008,5,57-60.
[6]陈小静,涂江华.我国猪肉产业及其贸易特征分析[J].中国市场,2008,44,18-19.
[7]刘合光,孙东升.中国生猪产业三大发展趋势与猪肉消费展望[J].中国畜牧杂志,2010,6,12-14,27.
[8]房慧,荣倩.我国猪肉产业面临的挑战[J].食品安全导刊,2010,9.20-27.
[9]屠康.肉类品质无损检测技术的研究进展[J].西北农林科技大学学报(自然科学版),2005,1,25-28.
[10]孔宪琴,黄素珍.肉品品质的无损检测方法[J].肉类研究,2008,8,66-69.
[11]张小宇.肉类无损检测新技术[J].肉类研究,2006,3,1-3.
[12]李静,操庆国.无损检测技术在农产品品质检验中的应用[J].中国科技信息,2010,21,79-80.
[13]潘立刚,张缙,陆安祥.马智宏,韩平.农产品质量无损检测技术研究进展与应用[J].农业工程学报,2008.S2,325-330.
[14]刘静,章程辉,黄勇平.无损检测技术在农产品品质评价中的应用[J].福建热作科技,2007,3,32-35.
[15]李敏,李宪华,奚星林,潘丙珍,庞世琦.无损检测技术在食品分析中的应用[J].检验检疫科学,2008,6,60-62.
[16]卜凡艳,韩剑众.无损检测技术在食品品质检测中的应用[J].食品工业科技,2007,7,221-224.
[17]孙通,徐惠荣,应义斌.近红外光谱分析技术在农产品/食品品质在线无损检测中的应用研究进展[J].光谱学与光谱分析,2009,1,122-126.
[18]刘燕德,刘涛,孙旭东,欧阳爱国,郝勇.拉曼光谱技术在食品质量安全检测中的应用[J].光谱学与光谱分析,2010,11,3007-3012.
[19]张涛,李军科,刘柱,袁士芳.食品科学中拉曼光谱研究进展[J].安徽农业科学,2007,33, 10563-10565.
[20]闫战科,张宏建,应义斌.THz技术在农产品/食品品质检测中的应用[J].光谱学与光谱分析,2007,11,2228-2234.
[21]刘芳,赵峰.超声波技术在食品生产检测和食品安全检测中的应用进展[J].福建分析测试,2008,4,27-31.
[22]周莉.超声波在食品工业中的应用的研究现状[J].肉类研究,2009,2,67-71.
[23]韩平,潘立刚,马智宏,陆安祥,王纪华.X射线无损检测技术在农产品品质评价中的应用[J].农机化研究,2009,10,6-10.
[24]田有文,王晓娟.基于高光谱图像技术的农产品品质无损检测[J].农机化研究,2009,10,220-226.
[25]王雷,乔晓艳,董有尔,张姝,尚艳飞.高光谱图像技术在农产品检测中的应用进展[J].应用光学,2009,4,639-645.
[26]童钊,廖桂平,李锦卫,金晶.机器视觉技术在农产品检测中的应用研究[J].农业网络信息,2008,11,18-21.
[27]应义斌,饶秀勤,赵匀,蒋亦元.机器视觉技术在农产品品质自动识别中的应用研究进展[J].农业工程学报,2000,3,4-8.
[28]周凝,刘宝林,王欣.核磁共振技术在食品分析检测中的应用[J].食品工业科技,2011,1,325-329.
[29]陈卫江,林向阳,阮榕生,何承云,朱榕璧,刘玉环.核磁共振技术无损快速评价食品水分的研究[J].食品研究与开发,2006,4,125-127.
[30]张哲,佟金.电子鼻和电子舌在食品检测中的研究和应用[J].华中农业大学学报,2005,S1,25-30.
[31]王俊,胡桂仙,于勇,周亦斌.电子鼻与电子舌在食品检测中的应用研究进展[J].农业工程学报,2004,2,292-295.
[32]吴莉莉,李辉,惠国华,袁超.传感器技术在农产品无损检测中的应用研究[J].江西农业学报,2010,5,]01-105.
[33]彭彦昆,张雷蕾.光谱技术在生鲜肉品质安全快速检测的研究进展[J].食品安全质量检测学报,2010,2,62-72.
[34]吴建虎,彭彦昆,陈菁菁,王伟,高晓东,黄慧.基于高光谱散射特征的牛肉品质参数的预测研究[J].光谱学与光谱分析,2010,7,1815-1819.
[35]吴建虎,彭彦昆,高晓东,陈菁菁,黄慧.基于VIS/NIR高光谱散射特征预测牛肉的嫩度[J].食品 安全质量检测技术,2009,1,20-26.
[36]吴建虎,彭彦昆,江发潮,王伟,李永玉,高晓东.牛肉嫩度的高光谱法检测技术[J].农业机械学报,2009,12,135-138,150.
[37]高晓东,吴建虎,彭彦昆,陈菁菁,陶斐斐.基于高光谱成像技术的牛肉大理石花纹的评估[J].农产品加工(学刊),2009,10,33-37.
[38]刘木华,蔡健荣,周小梅.x射线图像在农畜产品内部品质无损检测中的应用[J].农机化研究,2004,2,193-196.
[39]张迎雪.荧光检验能快速检测肉制品中的大肠杆菌[J].山东食品科技,2003,4,36-37.
[40]钟赛意,王善荣,刘寿春.超声波技术在动物性食品中的应用[J].肉类研究,2006,11,33-37.
[41]麻建国,周建军.超声波技术在食品检测中的应用[J].食品与发酵工业,1998,5,52-58.
[42]李正英,陈锦屏,陈忠军,杨俊峰.羊肉超声波嫩化技术的研究[J].食品科学,2006,12,413-416.
[43]庄玉亭,赵月兰.肉品新鲜度检测方法[J].河北科技大学学报.1999,2,63-65.
[44]陈纯.计算机图像处理技术与算法[M].北京：清华大学出版社,2003,7.
[45]李明静,刘贯勇,张志伟.计算机视觉在牛肉自动分级技术中的应用[J].肉类研究,2007,6,18-20.
[46]张哲,王楚端,王立贤,李俊奇,周伟良,李振宽.计算机视觉技术在猪眼肌肌内脂肪含量测定中的应用[J].猪业科学,2006,2,24-25.
[47]陆婉珍.现代近红外光谱分析技术(第二版).北京：中国石化出版社,2007,1.
[48]严衍禄.近红外光谱分析基础与应用.北京：中国轻工业出版社,2005.1.
[49]李民赞,韩东海,王秀.光谱分析技术及其应用.北京：科学出版社,2006,8.
[50]李炎,张月学,徐香玲,蒿若超.近红外反射光谱(NIRS)分析技术及其在农业上的应用[J].黑龙江农业科学,2008,1,105-108.
[51]阮治纲,李彬.近红外光谱分析技术的原理及在中药材中的应用[J].药物分析杂志,2011,2,408-417.
[52]高荣强,范世福.现代近红外光谱分析技术的原理及应用[J].分析仪器,2002,3,9-12.
[53]Prevolnik, M., Candek-Potokar, M., Skorjanc, D. Ability of NIR spectroscopy to predict meat chemical composition and quality-a review[J]. Czech Journal of Animal Science-UZPI,2004,49,500-510.
[54]Prieto, N., Roehe, R., Lavin, P., Batten, G., Andres, S. Application of near infrared reflectance spectroscopy to predict meat and meat products quality:A review[J]. Meat Science,2009,83,175-186.
[55]Rudbotten, R., Nilsen, B. N., Hildrum, K. I. Prediction of beef quality attributes from early post mortem near infrared reflectance spectra[J]. Food Chemistry,2000,69,427-436.
[56]Rudbotten, R., Mevik, B. H., Hildrum, K. I. Prediction and classification of tenderness in beef from non-invasive diode array detected NIR spectra[J]. Journal of near Infrared Spectroscopy 2001,9, 199-210.
[57]Park, B., Chen, Y. R., Hruschka, W. R., Shackelford, S. D., Koohmaraie, M. Principal component regression of near-infrared reflectance spectra for beef tenderness prediction[J]. Transactions of the Asae,2001,44,609-615.
[58]Jeyamkondan, S., Kranzler, G., Morgan, B., Rust, S. Predicting beef tenderness using near-infrared spectroscopy[J]. Monitoring Food Safety, Agriculture, and Plant Health,2004,5271,356-365.
[59]Rust, S. R., Price, D. M., Subbiah, J., Kranzler, G., et al. Predicting beef tenderness using near-infrared spectroscopy [J]. Journal of Animal Science,2008,86,211-219.
[60]Venel, C., Mullen, A. M., Downey, G., Troy, D. J. Prediction of tenderness and other quality attributes of beef by near infrared reflectance spectroscopy between 750 and 1100 nm; further studies[J]. Journal of near Infrared Spectroscopy,2001,9,185-198.
[61]Cozzolino, D., De Mattos, D., Martins, D. V. Visible/near infrared reflectance spectroscopy for predicting composition and tracing system of production of beef muscle[J]. Animal Science,2002,74. 477-484.
[62]Cozzolino, D., Martins, V., Murray, I. Visible and near infrared spectroscopy of beef longissimus dorsi muscle as a means of dicriminating between pasture and com silage feeding regimes[J]. Journal of near Infrared Spectroscopy,2002,10,187-193.
[63]Alomar, D., Gallo, C., Castaneda, M., Fuchslocher, R. Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS) [J]. Meat Science,2003,63,441-450.
[64]Togersen, G., Arnesen, J., Nilsen, B. N., Hildrum, K. I. On-line prediction of chemical composition of semi-frozen ground beef by non-invasive NIR spectroscopy[J]. Meat Science,2003,63,515-523.
[65]Liu, Y. L., Lyon, B. G., Windham, W. R., Realini, C. E., et al. Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study[J]. Meat Science,2003,65,1107-1115.
[66]Leroy, B., Lambotte, S., Dotreppe, O., Lecocq, H., et al. Prediction of technological and organoleptic properties of beef Longissimus thoracis from near-infrared reflectance and transmission spectra[J]. Meat Science,2004,66,45-54.
[67]Hildrum, K. I., Nilsen, B. N., Westad, F., Wahlgren, N. M. In-line analysis of ground beef using a diode array near infrared instrument on a conveyor belt[J]. Journal of near Infrared Spectroscopy,2004, 12,367-376.
[68]Prevolnik, M., Candek-Potokar, M., Skorjanc, D., Velikonja-Bolta, S., et al. Predicting intramuscular fat content in pork and beef by near infrared spectroscopy[J]. Journal of near Infrared Spectroscopy, 2005,13,77-85.
[69]Gangidi, R. R., Proctor, A., Pohlman, F. W., Meullenet, J. F. Rapid determination of spinal cord content in ground beef by near-infrared spectroscopy[J]. Journal of Food Science,2005,70,397-400.
[70]Prieto, N., Andres, S., Giraldez, F. J., Mantecon, A. R., Lavin, P. Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples[J]. Meat Science,2006,74,487-496.
[71]Bae, Y. M, Cho, S. I., Kim, Y. Y., Park,I. S., Hwang, K. Y. Estimation of freshness of beef using near-infrared spectroscopy [J]. Transactions of the Asabe.2006,49,557-561.
[72]De Marchi. M., Berzaghi, P., Boukha, A., Mirisola. M., Gallo, L. Use of near infrared spectroscopy for assessment of beef quality traits[J]. Italian Journal of Animal Science,2007,6,421-423.
[73]Prieto, N., Andres, S., Giraldez, F. J., Mantecon, A., Lavin, P. Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples[J]. Meat Science,2008,79,692-699.
[74]Prieto, N., Andres, S., Giraldez. F. J., Mantecon, A. R., Lavin. P. Discrimination of adult steers (oxen) and young cattle ground meat samples by near infrared reflectance spectroscopy (NIRS) [J]. Meat Science,2008,79,198-201.
[75]Ripoll, G., Alberti, P., Panea, B., Olleta, J. L., Sanudo, C. Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef[J]. Meat Science,2008,80,697-702.
[76]Andres, S., Silva, A., Soares-Pereira, A. L., Martins, C., et al. The use of visible and near infrared reflectance spectroscopy to predict beef M-longissimus thoracic et lumborum quality attributes[J]. Meat Science,2008,78,217-224.
[77]Rosenvold, K., Micklander, E., Hansen, P. W., Burling-Claridge, R., et al. Temporal, biochemical and structural factors that influence beef quality measurement using near infrared spectroscopy[J]. Meat Science,2009,82,379-388.
[78]Sierra, V., Aldai, N., Castro, P., Osoro, K., et al. Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy[J]. Meat Science,2008,78,248-255.
[79]Prieto, N., Ross, D. W., Navajas, E. A., Richardson, R.I., et al. Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy[J]. Animal,2011,5,155-165.
[80]Prieto, N., Ross, D. W., Navajas, E. A., Nute, G. R., et al. On-line application of visible and near infrared reflectance spectroscopy to predict chemical-physical and sensory characteristics of beef quality[J]. Meat Science,2009,83,96-103.
[81]Chan, D. E., Walker, P. N., Mills, E. W. Prediction of pork quality characteristics using visible and near-infrared spectroscopy[J]. Transactions of the Asae,2002,45,1519-1527.
[82]Gonzalez-Martin,I., Gonzalez-Perez, C., Hernandez-Mendez, J., Alvarez-Garcia, N., Andaluz, J. L. H. On-line non-destructive determination of proteins and infiltrated fat in Iberian pork loin by near infrared spectrometry with a remote reflectance fibre optic probe[J]. Analytica Chimica Acta,2002,453, 281-288.
[83]Gonzalez-Martin,I., Gonzalez-Perez, C., Hernandez-Mendez, J., Alvarez-Garcia. N. Mineral analysis (Fe, Zn, Ca, Na, K) of fresh Iberian pork loin by near infrared reflectance spectrometry-Determination of Fe, Na and K with a remote fibre-optic reflectance probe[J]. Analytica Chimica Acta,2002,468. 293-301.
[84]Cozzolino, D., Barlocco, N., Vadell, A., Ballesteros, F., Gallieta, G. The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenised pork muscle[J]. Lebensmittel-Wissenschaft Und-Technologie-Food Science and Technology,2003,36. 195-202.
[85]Geesink, G. H., Schreutelkamp, F. H., Frankhuizen, R., Vedder, H. W., et al. Prediction of pork quality attributes from near infrared reflectance spectra[J]. Meat Science,2003,65,661-668.
[86]Cozzolino, D., Murray,I. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy [J]. Lebensm.-Wiss. Technol.-Food Sci. Technol,2004,37,447-452.
[87]Hoving-Bolink, A., Vedder, H., Merks, J., De Klein, W., et al. Perspective of NIRS measurements early post mortem for prediction of pork quality[J]. Meat Science,2005,69,417-423.
[88]Savenije, B., Geesink, G. H., van der Palen, J. G. P., Hemke, G. Prediction of pork quality using
visible/near-infrared reflectance spectroscopy[J]. Meat Science,2006,73,181-184.
[89]Barlocco, N., Vadell, A., Ballesteros, F., Galietta, G., Cozzolino, D. Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy[J]. Animal Science,2006,82,111-116.
[90]Xing, J., Ngadi, M., Gunenc, A., Prasher, S., Gariepy, C. Use of visible spectroscopy for quality classification of intact pork meat[J]. Journal of Food Engineering,2007,82,135-141.
[91]Prevolnik, M., Candek-Potokar, M., Novic, M., Skorjanc, D. An attempt to predict pork drip loss from pH and colour measurements or near infrared spectra using artificial neural networks[J]. Meat Science, 2009,83,405-411.
[92]del Moral, F. G., Guillen, A., del Moral, L. G., O'Valle, F., et al. Duroc and Iberian pork neural network classification by visible and near infrared reflectance spectroscopy[J]. Journal of Food Engineering,2009,90,540-547.
[93]Prevolnik, M., Candek-Potokar, M., Skorjanc, D. Predicting pork water-holding capacity with NIR spectroscopy in relation to different reference methods [J]. Journal of Food Engineering.2010.98, 347-352.
[94]Monroy. M., Prasher, S., Ngadi, M. O., Karimi, Y. Pork meat quality classification using Visible/Near-Infrared spectroscopic data[J]. Biosystems Engineering,2010,107,271-276.
[95]Guillen, A., del Moral, F. G., Herrera, L. J., Rubio, G., et al. Using near-infrared spectroscopy in the classification of white and iberian pork with neural networks[J]. Neural Computing and Applications, 2010,19,465-470.
[96]Chou, C. H., Chyr, C. Y. L., Chen, R. L. C., Shaw, J. T. Evaluation of Freshness of Ground Pork Tenderloin by Visible/Near Infrared Spectroscopy [J]. Journal of Food and Drug Analysis,2010,18, 107-112.
[97]Fumiere.O., Sinnaeve, G., Dardenne, P. Attempted authentication of cut pieces of chicken meat from certified production using near infrared spectroscopy[J]. Journal of near Infrared Spectroscopy,2000,8, 27-34.
[98]Abeni, F., Bergoglio, G. Characterization of different strains of broiler chicken by carcass measurements, chemical and physical parameters and NIRS on breast muscle[J]. Meat Science,2001, 57,133-137.
[99]Cozzolino, D., Murray, I. Effect of sample presentation and animal muscle species on the analysis of meat by near infrared reflectance spectroscopy[J]. Journal of near Infrared Spectroscopy,2002,10, 37-44.
[100]Windham, W. R., Lawrence, K. C., Feldner, P. W. Prediction of fat content in poultry meat by near-infrared transmission analysis[J]. Journal of Applied Poultry Research,2003,12,69-73.
[101]Meullenet, J. F., Jonville, E., Grezes, D., Owens, C. M. Prediction of the texture of cooked poultry pectoralis major muscles by near-infrared reflectance analysis of raw meat[J]. Journal of Texture Studies,2004,35,573-585.
[102]Berzaghi, P., Dalla Zotte, A., Jansson, L. M., Andrighetto, I. Near-infrared reflectance spectroscopy as a method to predict chemical composition of breast meat and discriminate between different n-3 feeding sources[J]. Poultry Science,2005,84,128-136.
[103]Viljoen, M., Hoffman, L. C., Brand, T. S. Prediction of the chemical composition of freeze dried ostrich meat with near infrared reflectance spectroscopy [J]. Meat Science,2005,69,255-261.
[104]Tejerina, D., Lopez-Parra, M. M., Garcia-Torres, S. Potential used of near infrared reflectance spectroscopy to predict meat physico-chemical composition of guinea fowl (Numida meleagris) reared under different production systems[J]. Food Chemistry,2009,113,1290-1296.
[105]Samuel, D., Park, B., Sohn, M., Wicker, L. Visible-near-infrared spectroscopy to predict water-holding capacity in normal and pale broiler breast meat[J]. Poultry Science,2011,90,914-921.
[106]Cozzolino, D., Murray,I., Scaife, J., Paterson, R. Study of dissected lamb muscles by visible and near infrared reflectance spectroscopy for composition assessment[J]. Animal Science,2000,70,417-423.
[107]McGlone, V. A., Devine, C. E., Wells, R. W. Detection of tenderness, post-rigor age and water status changes in sheep meat using near infrared spectroscopy [J]. Journal of near Infrared Spectroscopy, 2005,13,277-285.
[108]Andres, S., Murray, I., Navajas, E. A., Fisher, A. V., et al. Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy[J]. Meat Science,2007,76,509-516.
[109]Viljoen, M., Hoffman, L., Brand, T. Prediction of the chemical composition of mutton with near infrared reflectance spectroscopy[J]. Small Ruminant Research,2007,69,88-94.
[110]Gonzalez-Redondo, P., Gomez, L. V., Herrero, L. G., Fernandez-Cabanas, V. M. Chemical composition of wild rabbit meat (Oryctolagus cuniculus) and viability of its prediction by near infrared spectroscopy[J]. Itea-Informacion Tecnica Economica Agraria,2010,106,184-196.
[111]Guy, F., Prache, S., Thomas, A., Bauchart, D., Andueza, D. Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS) [J]. Food Chemistry,2011,127, 1280-1286.
[112]Ding, H. B., Xu, R. J. Near-infrared spectroscopic technique for detection of beef hamburger adulteration[J]. Journal of Agricultural and Food Chemistry,2000,48,2193-2198.
[113]Kang, J. O., Park, J. Y., Choy, Y. H. Effect of grinding on color and chemical composition of pork sausages by near infrared spectrophotometric analyses[J]. Asian-Australasian Journal of Animal Sciences,2001,14,858-861.
[114]Ortiz-Somovilla, V., Espana-Espana, F., De Pedro-Sanz, E., Gaitan-Jurado, A. Meat mixture detection in Iberian pork sausages[J]. Meat Science,2005,71,490-497.
[115]Ortiz-Somovilla, V., Espa a-Espa a, F., Gaitan-Jurado, A., Perez-Aparicio, J., De Pedro-Sanz, E. Proximate analysis of homogenized and minced mass of pork sausages by NIRS[J]. Food Chemistry, 2007,101,1031-1040.
[116]Garcia-Rey, R.. Garcia-Olmo. J., De Pedro. E., Quiles-Zafra, R., Luque de Castro, M. Prediction of texture and colour of dry-cured ham by visible and near infrared spectroscopy using a fiber optic probe[J]. Meat Science,2005,70,357-363.
[117]Gaitan-Jurado, A., Ortiz-Somovilla, V., Espa a-Espa a, F., Perez-Aparicio, J., De Pedro-Sanz, E. Quantitative analysis of pork dry-cured sausages to quality control by NIR spectroscopy [J]. Meat Science,2008,78,391-399.
[118]Gonzalez-Martin, M.I., Bermejo, C. F., Hierro, J. M. H., Gonzalez, C. I. S. Determination of hydroxyproline in cured pork sausages and dry cured beef products by NIRS technology employing a fibre-optic probe[J]. Food Control 2009,20,752-755.
[119]ColleIl, C., Gou, P., Picouet, P., Arnau, J., Comaposada, J. Feasibility of near-infrared spectroscopy to predict a(w) and moisture and NaCl contents of fermented pork sausages[J]. Meat Science,2010,85, 325-330.
[120]于红樱,祁慧娜,臧勇军.近红外分析技术在肉与肉制品分析中的应用[J].肉类工业,2006,11,8-9.
[121]韩晨,贺稚非.肉品检测技术的研究进展[J].肉类研究,2006,12,21-23,32.
[122]王梅.肉制品检测技术的应用[J].肉类研究,2009,7,50-53.
[123]蒋耀庭,梁承红,陈菊娜.现代近红外光谱分析在食品检测中的应用[J].粮食与食品工业,2010,1,51-54.
[124]熊成,董庆利,曾静,胡孟晗.近红外光谱分析技术在肉品品质检测中的应用[J].食品与发酵工业,2010,12,141-145.
[125]成芳,廖宜涛,马君伟.猪肉品质无损检测研究进展[J].浙江大学学报(农业与生命科学版),2010,2,199-206.
[126]徐霞,成芳,应义斌.近红外光谱技术在肉品检测中的应用和研究进展[J].光谱学与光谱分析,2009,7,1876-1880.
[127]刘炜,俞湘麟,孙东东,陈鲁勇,刘全.傅立叶变换近红外光谱法快速检测鲜猪肉中肌内脂肪、蛋白质和水分含量[J].养猪,2005,3,47-50.
[128]姬瑞勤,王忠义,黄岚,侯瑞锋,徐志龙.鲜肉深层水分的无损检测方法研究[J].现代科学仪器,2006,1,119-121.
[129]姬瑞勤,黄岚,刘莉,王忠义.鲜肉水分近红外漫反射方法及实验研究[J].光谱学与光谱分析,2008,(8).1767-1771
[130]刘魁武,成芳,林宏建,孙通,许凯,胡雷秀,应义斌,徐惠荣.可见/近红外光谱检测冷鲜猪肉中的脂肪、蛋白质和水分含量[J].光谱学与光谱分析,2009,1,102-105.
[131]廖宜涛,樊玉霞,伍学千,成芳.猪肉肌内脂肪含量的可见/近红外光谱在线检测[J].农业机械学报,2010,9,681-684.
[132]Liao, Y.T., Fan, Y.X., Cheng, F. On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy[J]. Meat Science,2010,86,901-907.
[133]孟宪江,张铁强,白英奎,申铉国.利用BP神经网络光谱分类法研究肉品新鲜度[J].光谱实验室,2004,5,970-973.
[134]侯瑞锋,黄岚,王忠义,丁海曙,徐志龙.用近红外漫反射光谱检测肉品新鲜度的初步研究[J].光谱学与光谱分析,2006,12,2193-2196.
[135]赵杰文,翟剑妹,刘木华,蔡健荣.牛肉嫩度的近红外光谱法检测技术研究[J].光谱学与光谱分 析,2006,4,640-642.
[136]张德权,陈宵娜,孙素琴,李春红,张柏林,李勇,李淑荣,李庆鹏,周洪杰.羊肉嫩度傅里叶变换近红外光谱偏最小二乘法定量分析研究[J].光谱学与光谱分析,2008,11,2550-2553.
[137]杨勇,任健,王存堂,王文侠,康晶.鹅肉嫩度的测定及感官评价[J].河南工业大学学报(自然科学版),2009,6,29-32,87.
[138]张根伟,文星,王忠义,赵东杰,黄岚.基于稳态空间分辨光谱技术的猪肉嫩度测量方法研究[J].光谱学与光谱分析,2010,10,2793-2796.
[139]胡耀华,熊来怡,蒋国振,刘聪,郭康权,佐竹隆显.基于可见光和近红外光谱鲜猪肉蒸煮损失和嫩度检测的研究[J].光谱学与光谱分析,2010,11,2950-2953.
[]40]Cai, J., Chen, Q., Wan, X., Zhao, J. Determination of total volatile basic nitrogen (TVB-N) content and Warner-Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy[J]. Food Chemistry,2009,126,1354-1360.
[141]文星,梁志宏,张根伟,黄岚,王忠义.基于稳态空间分辨光谱的猪肉新鲜度检测方法[J].农业工程学报,2010,9,334-339.
[142]杨红菊,姜艳彬,候东军,赵丽丽.注胶肉的近红外光谱快速判别分析[J].肉类研究,2008,11,62-64.
[143]张宁,张德权,李淑荣,李庆鹏.近红外光谱结合SIMCA法溯源羊肉产地的初步研[J].农业工程学报,2008,12,309-312.
[144]胡耀华.郭康权,铃木孝范,野口刚,胡西红,佐竹隆显.基于可见光和近红外光谱的肉色检测[J].西北农林科技大学学报(自然科学版),2008,7,160-164.
[145]胡耀华,郭康权,野口刚,河野澄夫,佐竹隆显.基于近红外光谱检测猪肉系水力的研究[J].光谱学与光谱分析,2009,12,3259-3262.
[146]吴金亮,高新,殷云飞,陈吉红,杨国明.云南地方猪种肉质NIR指纹主成分分析和聚类分析研究[J].中国畜牧兽医,2010,11,94-99.
[147]杨志敏,丁武.原料肉中掺大豆蛋白的近红外检测技术的研究[J].肉类工业,2010,10,29-33.
[148]杨建松,孟庆翔,任丽萍,周振明,解祥学.近红外光谱法快速评定牛肉品质[J].光谱学与光谱分析,2010,3,685-687.
[149]廖宜涛,樊玉霞,伍学千,成芳.猪肉pH值的可见近红外光谱在线检测研究[J].光谱学与光谱分析,2010,3,681-684.
[150]朱迅涛.近红外分析技术在火腿肠检测中的应用[J].肉类研究,2002,2,42-44.
[151]赵丽丽,张录达,宋忠祥,李勇,严衍禄,马长伟.近红外光谱定量检测腌腊肉制品品质的研究[J].光谱学与光谱分析,2007,1,4649.
[152]Murray, A. C. How Much Fat Should Pig Muscle Contain? Advances in Pork Production13, Abstract 2002,34.
[153]Savitzky, A., Golay, M. J. E. Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry[J].1964,36,1627-1639.
[154]谢军,潘涛,陈洁梅,陈华舟,任小焕.血糖近红外光谱分析的Savitzky-Golay平滑模式与偏最小二乘法因子数的联合优选[J].分析化学,2010,3,342-346.
[155]芦永军,曲艳玲,宋敏.近红外相关光谱的多元散射校正处理研究[J].光谱学与光谱分析,2007,5,877-880.
[156]于海燕.黄酒品质和酒龄的近红外光谱分析方法研究.浙江大学博士学位论文.2007,1.
[157]Wold, S., Antti, H., Lindgren, F., hman, J. Orthogonal signal correction of near-infrared spectra[J]. Chemometrics and Intelligent Laboratory Systems,1998,44,175-185.
[158]张海东,赵杰文,刘木华.基于正交信号校正和偏最小二乘(OSC/PLS)的苹果糖度近红外检测[J].食品科学,2005,6,189-192.
[159]刘广军,戴冬梅,高洪涛.正交信号校正算法及其在光谱处理中的应用[J].山东建筑工程学院学报,2005,2,83-86,91.
[160]陆辉山.水果内部品质可见/近红外光谱实时无损检测关键技术研究.浙江大学博士学位论文.2006,8.
[161]单杨,朱向荣,许青松,梁逸曾.近红外光谱结合小波变换-径向基神经网络用于奶粉蛋白质与脂肪含量的测定[J].红外与毫米波学报,2010,2,128-131.
[162]夏俊芳,李小昱.脐橙VC含量近红外光谱消噪小波优化[J].农业机械学报,2009,4,143-146.
[163]Liang, L., Wang, B., Guo, Y., Ni, H., Ren, Y. A support vector machine-based analysis method with wavelet denoised near-infrared spectroscopy[J]. Vibrational Spectroscopy,2009,49,274-277.
[164]许禄,邵学广.化学计量学方法(第二版).北京：科学出版社.2004,90-175.
[165]金航峰,黄凌霞,吴迪,金佩华,楼程富.用最小二乘支持向量机的可见-近红外光谱测定蜂花粉贮存时间[J].红外与毫米波学报,2010,3,216-219.
[166]王义峰,张中卫.LSSVM在酒类近红外光谱检测中的应用[J].信息技术,2009,11,90-92.
[167]李晓丽,胡兴越,何勇.基于主成分和多类判别分析的可见-红外光谱水蜜桃品种鉴别新方法[J].红外与毫米波学报,2006,6,417-420.
[168]傅霞萍.水果内部品质可见/近红外光谱无损检测方法的实验研究.浙江大学博士学位论文.2008.4.
[169]谢丽娟.转基因番茄的可见/近红外光谱快速无损检测方法.浙江大学博士学位论文.2009.2.
[170]张晓曼.基于SVM的近红外光谱定性分析及其应用.浙江大学硕士学位论文.2008.6.
[171]葛红艳,邢光龙,王伟超.基于支持向量机分类的回归方法[J].电子技术,2008,9,44-46.
[172]Swatland, H. Infrared fiber optic spectrophotometry of meat[J]. Journal of Animal Science,1983,56, 1329-1333.
[173]Swatland, H. Color measurements on pork and veal carcasses by fiber optic spectrophotometry[J]. Canadian Institute of Food Science and Technology Journal,1986,19,170-173.
[174]Swatland, H. Post-mortem spectrophotometry of pork and beef using quartz optical fibres[J]. Meat Science,1986,17,97-106.
[175]Murray, I. The NIR spectra of homologous series of organic compounds[J]. In Proceedings of the International NIR/NIT Conference; Hollo, J., Kaffka, K. J., Gonczy, J. L., Eds.; Akademiai Kiado:Budapest, Hungary,1986; pp 13-28.
[176]Williams. P., Norris. K. Near-infrared technology in the agricultural and food industries, American Association of Cereal Chemists. Inc[M],1987.17-34.
[177]Shenk, J., Workman, J., Westerhaus, M. Application of NIR spectroscopy to agricultural products[J]. Practical Spectroscopy Series,1992,13,383-431.
[178]Nanke, K., Sebranek, J., Olson, D. Color characteristics of irradiated vacuum packaged pork, beef, and turkey[J]. Journal of Food Science-Chicago,1998,63,1001-1006.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |