Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy

This study was implemented to evaluate the potential of visible and near infrared reflectance (NIR) spectroscopy to predict sensory characteristics related to the eating quality of lamb meat samples. A total of 232 muscle samples from Texel and Scottish Blackface lambs was analyzed by chemical procedures and scored by assessors in a taste panel (TP). Then, these parameters were predicted from Vis/NIR spectra. The prediction equations showed that the absorbance data could explain a significant but relatively low proportion of the variability (R(2)<0.40) in the taste panel traits (texture, juiciness, flavour, abnormal flavour and overall liking) of the lamb meat samples. However, a top-tail approach, looking at the spectra of the 25 best and worst samples as judged by TP assessors, provided more meaningful results. This approach suggests that the assessors and the spectrophotometer were able to discriminate between the most extreme samples. This may have practical implications for sorting meat into a high quality class, which could be branded, into a low quality class sold for a lower price for less demanding food use. Regarding the chemical parameters, both intramuscular fat and water could be more accurately predicted by Vis/NIR spectra (R(2)=0.841 and 0.674, respectively) than sensory characteristics. In addition, the results obtained in the present study suggest that the more important regions of the spectra to estimate the sensory characteristics are related to the absorbance of these two chemical components in meat samples.     

Prediction of technological and organoleptic properties of beef Longissimus thoracis from near-infrared reflectance and transmission spectra

Technological and organoleptic properties of beef cuts were predicted by near-infrared (NIR) spectroscopy. Spectra were collected on 189 beef Longissimus thoracis muscle samples using, transmission (NIRT) and reflectance with a probe. Quality assessment and NIR recordings were performed on sliced loin after 2 and 8 days ageing. Partial least squares regression yielded determination coefficients of cross-validation (R(2)(cv)) of 0.12-0.25 for the prediction of Warner-Bratzler Peak Shear Force in reflectance and 0.15-0.41 in transmission. Higher R(2)(cv) were obtained for L* parameter (0.83-0.85), a* (0.39-0.49) and b* (0.73-0.75) with reflectance. Predictions of drip loss and cooking loss were less accurate with a R(2)(cv) range of 0.38 to 0.54 and 0.25 to 0.47, respectively. The NIR spectra collected on fresh meat show good potential to predict CIE L* and b* parameters in reflectance mode.     

近红外漫反射光谱检测损伤猕猴桃内部品质

研究贮藏期间损伤猕猴桃内部品质与其近红外漫反射光谱之间的关系。利用近红外光谱(12000～4000cm-1)技术和多元线性回归(multiple linear regression,MLR)、主成分回归(principal component regression,PCR)和偏最小二乘法(partial least squares,PLS)3种校正方法分别对损伤华优猕猴桃在2℃条件下贮藏4周期间的可溶性固形物含量、pH值和硬度进行定量分析;并对比吸光度原始光谱、一阶微分和二阶微分3种不同预处理方法的PLS模型校正结果。结果表明：一阶微分预处理方法时,应用PLS建立的可溶性固形物含量、pH值和硬度校正模型的效果最佳;预测集样品预测值与测量值之间的相关系数分别为0.812、0.703、0.919,预测均方根误差分别为0.749、0.153、1.700。说明应用近红外漫反射技术检测贮藏期间损伤猕猴桃的内部品质是可行的。     

Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study

Color, instrumental texture, and sensory attributes of steaks from 24 beef carcasses at 2, 4, 8, 14, and 21 days post mortem were predicted by visible/near infrared (visible/NIR) reflectance spectroscopy in 400-1080 nm region. Predicting the Hunter a, b, and E* yielded the coefficient of determination (R(2)) in calibration to be 0.78-0.90, and R(2) was between 0.49 and 0.55 for tenderness, Hunter L, sensory chewiness and juiciness. The prediction R(2) for tenderness was in the range of 0.22-0.72 when the samples were segregated according to the aging days. Based on partial least square (PLS) model predicted tenderness, beef samples were classified into tender and tough classes with a correct classification of 83%. Soft independent modeling of class analogy of principal component analysis (SIMCA/PCA) model of measured tenderness showed great promise in the classification of tender and tough meats with over 96% success.     

Near‐infrared reflectance spectroscopy for determining end‐point temperature of heated fish and shellfish meats

Attempts have been made to assess the end‐point temperature (EPT) of heated fish and shellfish meats by near‐infrared (NIR) reflectance spectroscopy. Since the presence of water affects NIR spectra, the influence of the water content of samples on the performance of NIR spectroscopy for determining EPT was also evaluated. Blue marlin (Makaira mazara), skipjack (Katsuwonus pelamis), red sea bream (Pagrus major), kuruma prawn (Penaeus japonicus) and scallop (Patinopecten yessoensis) meats were heat‐treated at different temperatures (5 °C intervals between 60 and 100 °C). NIR spectra were measured at 2 nm intervals between 1100 and 2500 nm. A stepwise multiple linear regression method was used to develop a calibration curve. Changes in NIR reflectance spectra upon heat treatment were related to the heating temperature. Moreover, the interference from the variation in water content on the prediction of EPT was eliminated by selecting appropriate wavelengths. Plotting of NIR‐predicted temperatures determined from d² log(1/R) at four specific wavelengths against the actual EPT revealed a promising linear relationship with correlation coefficients between 0.94 and 0.98. In the temperature range 60–100 °C, NIR reflectance spectroscopy was able to detect EPT of heated fish and shellfish meats with a prediction error of 1.9–3.1%. © 2002 Society of Chemical Industry     

Modelling of sensory and instrumental texture parameters in processed cheese by near infrared reflectance spectroscopy

This study investigated the application of near infrared (NIR) reflectance spectroscopy to the measurement of texture (sensory and instrumental) in experimental processed cheese samples. Spectra (750 to 2498 nm) of cheeses were recorded after 2 and 4 weeks storage at 4 degrees C. Trained assessors evaluated 9 sensory properties, a texture profile analyser (TPA) was used to record 5 instrumental parameters and cheese 'meltability' was measured by computer vision. Predictive models for sensory and instrumental texture parameters were developed using partial least squares regression on raw or pre-treated spectral data. Sensory attributes and instrumental texture measurements were modelled with sufficient accuracy to recommend the use of NIR reflectance spectroscopy for routine quality assessment of processed cheese.     

可见/近红外光谱技术无损检测新鲜鸡蛋蛋白质含量的研究

鸡蛋是一种重要的食品,蛋白质是鸡蛋的主要营养成分。本研究利用可见近红外反射光谱技术无损检测新鲜鸡蛋的蛋白质含量。使用光谱仪获取新鲜鸡蛋在400~1100 nm波段范围内的漫反射光谱;分别使用多元散射校正(MSC)法和一阶导数法(1-D)对反射光谱进行预处理;对反射光谱、MSC处理光谱和1-D光谱,使用逐步回归法判别法选择最优波长组合,建立多元线性回归模型,使用全交叉验证法验证模型。结果表明,可见/近红外反射光谱经过多元散射校正后,确定的10个最优波长(400、403.16、407.9、714.6、715、715.58、970.4、970.75、973和974.45 nm)组合建立模型的校正和验证结果最好:选定模型的校正结果为R=0.92,SEC=0.42%;验证结果为Rcv=0.89,SECV=0.47%。研究表明可见/近红外反射光谱技术可以较好的预测新鲜鸡蛋的蛋白质含量,本研究可为可见近红外光谱技术在鸡蛋营养成分的快速检测提供一定的理论基础。     

基于便携式近红外光谱仪的黄桃腐败时间快速预测

目的：利用便携式近红外（near infrared, NIR）光谱仪与化学计量学方法预测黄桃的腐败时间。方法：利用便携式NIR光谱仪采集黄桃样本的漫反射光谱,通过光谱预处理方法提高数据特征,采用偏最小二乘法（partial least squares, PLS）建立黄桃腐败时间的预测模型。通过均方根误差（root mean square error, RMSE）和决定系数（coefficient of determination, R²）评估模型的预测效果。结果：模型对黄桃腐败时间预测的R²为0.63,RMSE为4.09 d。结论：NIR光谱结合化学计量学方法能够实现黄桃腐败时间的无损、准确预测。     

近红外光谱法快速检测婴儿配方奶粉中的脂肪酸含量

采用傅里叶近红外光谱结合偏最小二乘法(PLS)法建立了测定婴儿配方奶粉中的总脂肪酸、饱和脂肪酸和不饱和脂肪酸含量的近红外数学模型,并通过交互验证和外部检验两种方式考察了近红外数学模型的可靠性。通过选择不同的波长范围,采用平滑、矢量归一化、一阶求导、二阶求导和散射校正对近红外光谱进行处理,总脂肪酸、饱和脂肪酸和不饱和脂肪酸的校正模型相关系数(R2)分别为0.9337、0.9374、0.9020,RPD分别为3.63、3.65、2.90。结果表明近红外数学模型具有良好的预测性能。采用建立的模型对验证集中的20个婴儿配方奶粉样品进行预测,总脂肪酸含量、饱和脂肪酸和不饱和脂肪酸的预测值与化学测定值之间经配对t检验分析,与常规化学方法得到的检验结果无显著差异。     

近红外光谱结合膜富集技术测定大米中毒死蜱农药残留

本文以纤维滤膜富集大米中的微量农药残留,提高近红外光谱技术的检测限。向阴性大米样本中喷洒不同浓度毒死蜱标准溶液,制备含农药残留大米样品,以乙腈为溶剂提取大米中的毒死蜱农药,用氮吹仪将提取液浓缩后,使用滤纸富集提取液中的农药,真空冷冻干燥,采集滤纸的近红外漫反射光谱。运用特征波长筛选方法优选特征变量,建立大米中毒死蜱农药残留的近红外光谱分析模型。结果表明,利用联合区间偏最小二乘法方法从全光谱区优选出子区间[3 4 5 10],进一步用遗传算法从子区间中优选80个变量时,所建模型性能最好。在0.46~11.20 mg/kg浓度范围内,模型对预测集样本的相关系数为0.9798,预测均方根误差为0.604 mg/kg,将该模型预测4个未知农药含量的大米样本,其预测值与实际测量值具有较好的一致性。研究表明该方法能较好地快速检测大米中微量农药残留。     

Screening of acrylamide contents in potato crisps using process variable settings and near-infrared spectroscopy

In order to study the formation of acrylamide in potato crisps during processing, an experimental design was set up. The design variables were drying time (6 levels), frying temperature (2 levels) and frying time (8 levels). The design contained 36 samples, which were analysed for acrylamide contents using LC high-resolution mass spectroscopy (LC-HRMS), and fat contents using the Soxhlet apparatus. Prior to analysis, all potato crisp samples were ground and analysed on an NIRSystems 6500 near-infrared (NIR) spectrometer. The acrylamide contents were modelled by: (i) design variables using multiple linear regression, (ii) NIR spectra using partial least squares regression (PLSR) and (iii) design variables and NIR spectra in combination using a novel technique combining least squares regression on the former, and PLSR on the latter. The results showed that the NIR spectra alone or in combination with the design variables gave better prediction models for acrylamide than the design variables alone. This implies that the spectra contain chemical information that is not purely a result of the processing variables that were investigated in this experiment. NIR spectroscopy is proposed as a possible tool for screening and identification of potato crisps with a high acrylamide content.     

近红外光谱的苹果内部品质在线检测模型优化

利用近红外光谱技术在线检测水果内部品质的关键是获取精度高稳健性好的定量分析模型。研究开发了短波近红外光谱苹果品质在线检测系统,试验时苹果样本传输速度为5个/s,以漫反射方式采集,有效光谱范围为500~1100 nm。经光谱强度标准化校正后,有比较的采用遗传算法、连续投影算法和蚁群优化算法等提取特征变量,分别建立偏最小二乘模型,同时分析了这三种方法提取光谱特征变量的搜索机制。特征变量提取方法建立的预测模型所用变量显著减少,预测效果均优于全光谱模型,且能提高运算速度,增强模型的稳健性;其中又以蚁群优化算法的模型预测能力最佳,预测集相关系数R为0.9358,预测均方根误差RMSEP为0.2619。研究结果表明,近红外光谱结合特征变量提取方法可以建立高效的苹果可溶性固形物含量在线检测模型,在产业化应用方面具有很大潜力。     

Prediction of sensory characteristics of beef by near-infrared spectroscopy

Sensory hardness, tenderness and juiciness of M. Longissimus dorsi muscles from 10 beef carcasses at three ageing stages were predicted by near-infrared (NIR) spectroscopic analysis in the reflection (NIRR) and transmission modes (NIRT) during 14 days ageing at 2°C. Predicting the sensory variables hardness and tenderness from NIRR measurements using principal component regression (PCR), yielded correlation coefficients in the range 0·80-0·90. The root mean square errors of prediction for the predictions of hardness and tenderness were in the range 0·5-0·7, given in sensory assessment units. Juiciness was not well predicted. Prediction of sensory variables from NIRT measurements did not give satisfactory results. Including samples from all carcasses, cows and young bulls in the models resulted in good predictions from NIRR measurements of frozen and thawed samples. However, the best prediction results were generally obtained from separate calibrations of the samples from the bulls. The potential of NIR spectroscopy in the prediction of sensory variables in whole meat needs to be further investigated on a larger number of samples with different breeds, animals and process treatments included.     

Mineral equilibrium in commercial curd and predictive ability of near-infrared spectroscopy

Curd samples (n = 83) from 3 European dairy companies were analyzed for micellar and soluble mineral fractions content using inductively coupled plasma optical emission spectrometry as a gold standard method. The same curd samples were analyzed through 3 different near-infrared (NIR) instruments, and NIR spectra were merged with reference data. Prediction equations were developed using modified partial least squares analysis, and the accuracy of prediction was evaluated through leave-one-out cross validation. Overall, NIR spectroscopy was capable of predicting micellar and soluble mineral fractions in curd, but with differences among instruments. Fitting statistics showed that the visible NIR instrument in reflectance mode outperformed the NIR instrument in transmittance mode as well as the portable NIR instrument in reflectance mode. Prediction accuracies for most of the analyzed mineral fractions can be used for curd quality control in dairy companies and to aid in decision-making during the cheesemaking process.     

基于近红外光谱技术的腐竹脂肪定量分析

采用近红外光谱技术结合化学计量学方法,建立腐竹脂肪含量的快速分析方法。收集不同生产线、不同时间的腐竹样本180 份,利用积分球附件采集漫反射光谱（4 000～10 000 cm－1）。为消除颗粒散射影响和光谱基线漂移,二阶导数和卷积平滑用于光谱预处理。采用反向区间偏最小二乘法、组合区间偏最小二乘法、搜索组合移动窗口偏最小二乘法和遗传偏最小二乘法优化建模变量,最终构建了定量预测模型。结果显示,4 种方法均可有效地提取信息变量、降低模型维度、提高预测性能;遗传偏最小二乘法一次优选获得143 个变量,构建的模型性能最佳,其校正相关系数、校正均方根误差、预测相关系数、预测均方根误差分别为0.96、0.95、0.92和1.17。研究表明,经过信息变量提取后所构建的近红外模型简单、预测精度高,可用于腐竹脂肪含量的日常监测。     

Differentiation of Beef and Kangaroo Meat by Visible/Near-Infrared Reflectance Spectroscopy

A visible/near-infrared (NIR) spectroscopic method was developed with canonical discriminant analysis and stepwise multiple linear regression to differentiate beef from kangaroo meat. Results showed that beef and kangaroo meat could be separated when samples were analyzed by spectrophotometry as minced meat or cut meat. For minced meat, scatter correction and derivative treatment of reflectance spectra improved classification. For cut meat, original reflectance spectra produced better classification. Overall classification accuracy was 83% to 100%, and no kangaroo meat was misclassified. NIR spectroscopy might be an efficient test method for species identification.     

可见/近红外漫反射光谱无损检测甜柿果实硬度

该研究的目的是建立可见/近红外漫反射光谱无损检测甜柿果实硬度的数学模型,评价可见/近红外漫反射光谱无损检测甜柿果实硬度的应用价值。果实硬度采用果皮脆性、果皮强度和果肉平均硬度作为评价指标。在可见/近红外光谱区域(400～2 500 nm),采用改进偏最小二乘法,对比分析了不同导数处理、不同散射及标准化处理的甜柿果实硬度定标模型。结果表明,对于果皮强度和果皮脆性,采用最小偏二乘法、一阶导数处理和标准多元离散校正处理建立的定标模型预测效果较好,RP2分别为0.858和0.862,SEP分别为0.094和0.157,RPD分别为2.47和2.63。对于果肉平均硬度,采用改进偏最小二乘法、一阶导数处理和标准正常化和去散射处理建立的定标模型预测效果较好,RP2为0.82,SEP为0.063,RPD为2.35。因此,可见/近红外漫反射光谱无损检测技术可用于甜柿果实硬度的无损检测。     

Near-Infrared Spectroscopy Determination of Physical and Chemical Characteristics in Beef Cuts

The qualities of beef cuts were compared with near-infrared (NIR) spectroscopy readings using reflectance, transmittance and a fiber optic probe. Multiple linear regression analyses were used to select the optimum wavelengths for estimating beef properties. High multiple correlation coefficients (R) were obtained for Warner-Bratzler shear value (R = 0.798?0.826), protein (R = 0.822?0.904), moisture (R = 0.895?0.941), fat (R = 0.890?0.965) and energy content (R = 0.899?0.961) with each reflectance, transmittance and using the fiber optic probe. Total pigment content also highly correlated with optical densities using transmittance (R = 0.946) and the fiber optic probe (R = 0.893). NIR with a fiber optic probe is a useful tool for determining physical and chemical characteristics of beef.     

基于近红外光谱的茶叶高氯酸盐污染水平研究

目的 建立一种基于近红外光谱（near-infrared spectroscopy,NIR）分析技术快速定量茶叶中高氯酸盐污染水平的预测模型。方法 采集不同产地的91份茶叶干样,通过傅里叶变换近红外光谱扫描获得样品的近红外漫反射光谱,使用液相色谱质谱法测定茶叶样品中的高氯酸盐含量,以参考量限0.75 mg/kg为标准将样品分为两类;利用偏最小二乘判别分析（partial least squares-discriminant analysis, PLS-DA）建模建立高氯酸盐含量范围的预测模型,同时使用一阶导（1st）、连续小波变换（continuous wavelet transform, CWT）、多元散射校正（multiplicative scatter correction,MSC）、标准正态变换（standard normal variate,SNV）等光谱预处理和竞争自适应重加权（competitive adaptive reweighted sampling,CARS）采样波长筛选技术对判别模型进行优化,最后通过预测集样品对模型进行验证。结果 使用原始光谱建立的模型能够初步实现对高氯酸盐含量范围的预测,而使用光谱预处理扣除光谱中的背景信息,结合MSC和CARS方法共同处理后,模型的预测正确度显著改善,误判样品下降至3个,预测正确率提高至88.5%。结论 本方法表明近红外光谱技术可以为茶叶中高氯酸盐污染水平分析提供一种新方法,对茶叶产业高质量发展具有重要的实际意义。