Prediction of chemical,physical and sensory data from process parameters for frozen cod using multivariate analysis

Physical, chemical and sensory quality parameters were determined for 115 cod (Gadus morhua) samples stored under varying frozen storage conditions. Five different process parameters (period of frozen storage, frozen storage temperature, place of catch, season for catching and state of rigor) were varied systematically at two levels. The data obtained were evaluated using the multivariate methods, principal component analysis (PCA) and partial least squares (PLS) regression. The PCA models were used to identify which process parameters were actually most important for the quality of the frozen cod. PLS models that were able to predict the physical, chemical and sensory quality parameters from the process parameters of the frozen raw material were generated. The prediction abilities of the PLS models were good enough to give reasonable results even when the process parameters were characterised by ones and zeroes only. These results illustrate the application of multivariate analysis as an effective strategy for improving the quality of frozen fish products. © 1998 Society of Chemical Industry.     

牛肉酶解物对牛肉特征香味形成的影响研究

以五种不同水解度牛肉酶解物分别制备得到5种热反应牛肉香精。利用定量感官分析和GCMS-O分析考察了5种热反应牛肉香精香气成分的变化。结果显示,添加DH29.13%酶解液的牛肉香精样品其牛肉味、肉香味和仿真度相比于其它样品是最强的。GC-O分析也发现DH29.13%的牛肉酶解液赋予形成了种类较多的香气活性化合物,而没有添加牛肉酶解物的肉味香精缺失了这些特征化合物。利用PLSR进行了香气活性化合物、感官评价和牛肉酶解液的肽分子量分布之间的相关性分析,进一步解释了DH为29.13%的牛肉酶解液是赋予热加工牛肉风味的最合适风味前体。     

地沟油模式识别技术的研究

借鉴中药指纹图谱的研究思路,在优选的色谱条件下,采用气相色谱法对16个样品进行分析,利用相似度软件筛选出12个共有峰,通过方法学验证,12个共有峰精密度、稳定性的RSD分别小于2.6%和6.1%。在此基础上,采用主成分分析（PAC）和偏最小二乘判别（PLS-DA）,对16个样品的12个共有峰进行化学计量学研究,建立地沟油模式识别模型。利用所建立的地沟油模式识别模型对16个样品（7个正品油,9个地沟油）进行判别分析,结果表明:所建立的模式识别模型能较好的区分地沟油和正品油。本方法仅使用气相色谱（GC）,结合化学计量学分析,简单快速,便于推广,可用于地沟油的快速鉴别。      

Application of multivariate statistical methods to extend the authenticity control of flavour constituents of apple juice

 The distribution of 15 aroma constituents, including the (R)- and (S)-enantiomers of ethyl 2-methylbutanoate, in 30 commercially available and laboratory prepared apple juices was evaluated by multivariate statistical methods. Principal component analysis and cluster analysis revealed ethyl 3-methylbutanoate, 3-methylbutyl acetate and (R)-ethyl 2-methylbutanoate as key components for the differentiation of apple juices. In freshly pressed and some commercial juices ethyl 3-methylbutanoate and 3-methylbutyl acetate were not present, and the (S):(R) ratio of ethyl 2-methylbutanoate was found to be >99 : 1%. Another group of commercial products was separated in which the amounts of ethyl 3-methylbutanoate and 3-methylbutyl acetate exceeded 1 μg/l and 180 μg/l, respectively, and in which the (S):(R) ratio of ethyl 2-methylbutanoate was <97 : 3%. The statistical data confirmed the high selectivity of apple fruit tissue in biosynthesizing 2-methylbutyl and 2-methylbutanoate esters, and could also be used to extend the results of enantio-multidimensional gas chromatography and other authenticity analyses. Received: 20 April 1998 / Revised version: 23 June 1998     

化学计量学——光度法在食品多组份分析中的应用评述

本文对化学计量学光度法在食品多组分分析中的应用进行了评述,并简要介绍和讨论了多元线性回归、因子分析法、主成分回归、偏最小二乘法、卡尔曼滤波、人工神经网络及其在食品分析中的应用。引用文献90篇。     

基于LF-NMR弛豫特性的煎炸油总极性化合物含量定量建模方法

将低场核磁共振（low field nuclear magnetic resonance,LF-NMR）分析技术应用于煎炸油脂总极性化合物（total polar compounds,TPC）含量的预测。采用柱层析方法测定油脂样品的TPC含量作为测定值,采集油脂样品的LF-NMR弛豫特性（峰起始时间T21、T22、T23相应的峰面积比例S21、S22、S23、单组分弛豫时间T2W）,分别利用向后筛选多元回归分析、主成分回归分析和偏最小二乘回归分析建立LF-NMR弛豫特性与TPC含量的回归方程,比较3 种模型的校正集和预测集的决定系数与均方根误差,最终确定最优模型为偏最小二乘回归模型。应用此模型预测预测集样品TPC含量,决定系数R2可达0.928,预测集均方根误差为0.568%,模型稳定。     

Variable selection for monitoring sickness behavior in lactating dairy cattle with the application of control charts

The present observational study investigated the application of multivariate cumulative sum (MCUSUM) control charts by including variables selected by principal component analysis and partial least squares (PLS) regression to identify sickness behavior in dairy cattle. Therefore, sensor information (24 variables) was collected from 480 milking cows on a German dairy farm between September 2018 and December 2019. These variables were gathered in potentially different scenarios on farm. In total, data from 749 animals were available for evaluation. Variables were chosen based on the information of 499 cows (62 healthy; 437 sick) with 93,598 observations. The available diagnoses were collected together to form 1,025 sickness events. Hence, the different numbers of selected variables were included into the MCUSUM control charts. The performance of the MCUSUM control charts was evaluated by a 10-fold cross validation; hence, 90% of the original data set (749 cows) represented the training data, and the remaining 10% was used to test the training results. On average, the 10 training data sets included 124,871 observations with 1,392 sickness events, and the 10 testing data sets included, on average, 13,704 observations with 153 sickness events. The MCUSUM generated from the variables selected by principal component analysis showed comparable results in training and testing in all scenarios; therefore, 70.0 to 97.4% of the sickness events were detected. The false-positive rates ranged from 8.5 to 29.6%, and thus they created at least 2.6 false-positive alerts per day in testing. The variables selected by the PLS regression approach showed comparable sickness detection rates (70.0–99.9%) as well as false-positive rates (8.2–62.8%) in most scenarios. The best performing scenario produced 2.5 false-positive alerts in testing. Summarizing, both approaches showed potential for practical implementation; however, the PLS variable selection approach showed fewer false positives. Therefore, the PLS regression approach could generate a more reliable sickness detection algorithm, if combined with MCUSUM control charts, and considered for practical implementation.     

Development of a flavour fingerprint by GC‐MS and GC‐O combined with chemometric methods for the quality control of Korla pear (Pyrus serotina Reld)

Based on solid‐phase micro‐extraction/gas chromatography‐mass spectrometry (SPME/GC‐MS) and gas chromatography‐olfactometry (GC‐O), a new method was described for the first time to establish flavour fingerprint for the quality control of Korla pear. Twenty‐three batches of Korla pear collected from different regions in Xinjiang, China were analysed via GC‐MS, among which 21 were selected to establish the fingerprint. The results of the analysis were combined with GC‐O assessment to obtain 26 common odour‐active compounds as the characteristics of the Korla pear flavour fingerprint. The method for flavour fingerprint analysis was then validated on the basis of the relative retention time and relative peak area of the common peaks, sample stability and similarity analysis. The similarities in the 21 Korla pear samples were more than 0.80, thereby indicating that the samples from different batches were consistent to a particular extent. The chemometrics method (principal component analysis, PCA) was performed to develop a flavour fingerprint that is suitable for identifying and differentiating Korla pears and can be used for quality control.     

基于7种脂肪酸含量测定和化学计量学的沙棘籽油质量评价研究

目的 建立气相色谱法同时测定沙棘籽油中7种脂肪酸的含量, 并结合化学计量学进行质量评价。方法 样品经甲酯化后, 采用DB-23色谱柱(60 m×0.25 mm, 0.25 μm)测定脂肪酸含量。通过聚类分析(cluster analysis, CA)、主成分分析(principal component analysis, PCA)和正交偏最小二乘判别分析(orthogonal partial least squares discriminant analysis, OPLS-DA), 对19批沙棘籽油进行质量评价。结果 7种脂肪酸甲酯在各自浓度范围内线性关系良好, r≥0.999, 棕榈酸(palmitic acid)、棕榈油酸(palmitoleic acid)、硬脂酸(stearic acid)、油酸(oleic acid)、亚油酸(linoleic acid, LA)、γ-亚麻酸(γ-linolenic acid, γ-LA)和α-亚麻酸(α-linolenic acid, α-LA)平均加样回收率为91.9%~97.4%。通过聚类分析将样品聚为4类, 根据主成分分析的样品综合得分进行排名。同时结合正交偏最小二乘判别分析, 最终筛选得到油酸、亚油酸和α-亚麻酸是引起沙棘籽油质量差异的标志物, 与主成分分析结果基本一致。结论 通过脂肪酸含量测定并结合化学计量法, 可以对沙棘籽油的质量进行评价, 为质量控制提供参考依据。     

用气相色谱法预测挥发性有机试剂的沸点

根据不同结构基团的挥发性有机试剂在不同极性气相色谱柱上的保留行为,用多元线性回归(MLR)方法,建立了挥发性有机试剂沸点与不同极性色谱柱上保留时间之间的关系模型,并根据各模型相关系数与概率值定量说明了模型中不同参数引入时的统计意义.结果表明,与单一极性色谱柱上的保留时间相比,采用不同极性色谱柱上的保留时间,可以更好地预测有机试剂的沸点,并且更具统计学意义.     

Water retention properties and solubility of the myofibrillar proteins: Interrelationships and possible value as indicators of the gel strength in cod surimi by a multivariate data analysis

The quality of surimi made from fish aged on ice for up to 14 days was evaluated by compression test on a model product. Water holding capacity (WHC) and pH in the fish mince, and protein content, pH, salt solubility of the myofibrillar proteins and hydration index of the insoluble proteins (HUP) in the surimi, were determined. Their interrelationships and utility as indicators of surimi quality were evaluated by a multivariate data analysis. The main tendencies of variation among the chemical parameters were examined by the use of principal component analysis. Correlations between the chemical parameters (X-variables) and the gel strength of surimis (Y-variable) were studied by the use of partial least squares regression. Age of the raw material and protein content of the surimi seemed to be the main determinants of the gel strength. Among the remaining variables only HIIP showed a potential value as indicator of the gel strength. WHC and HIIP seemed to reflect two different water retention properties of the proteins.     

Green method for determination of phenolic compounds in mung bean (Vigna radiata L.) based on near‐infrared spectroscopy and chemometrics

Mung bean grains exhibit high level of antioxidant activity due the presence of phenolic compounds. Near‐infrared spectroscopy (NIRS), in conjunction with chemometrics, was used to develop a rapid, nondestructive, chemical free and easy‐to‐use method to determine these compounds in sixty genotypes of mung bean. NIRS calibration curve with high‐performance liquid chromatography (HPLC) as reference method was used to determine phenolic compounds (catechin, chlorogenic acid, caffeic acid, p‐coumaric acid, t‐ferulic acid, vitexin, isovitexin, myricetin, quercetin and kaempferol). It was observed that partial least square regression (PLSR) model in the wavelength range of 1600–2500 nm with standard normal variate (SNV) and linear baseline correction (LBC) as preprocessing techniques can measure phenolic compound accurately (R² > 0.987) with root‐mean‐square error less than 1.82%. This study shows that NIRS along with chemometrics is an accurate method to estimate the phenolic compounds rapidly and nondestructively.     

A correlative study on data from pork carcass and processed meat (Bauernspeck) for automatic estimation of chemical parameters by means of near-infrared spectroscopy

The use of near-infrared spectroscopy (NIRS) is proposed in this study for the characterization of the quality parameters of a smoked and dry-cured meat product known as Bauernspeck (originally from Northern Italy), as well as of some technological traits of the pork carcass used for its manufacturing. In particular, NIRS is shown to successfully estimate several key quality parameters (including water activity, moisture, dry matter, ash and protein content), suggesting its suitability for real time application in replacement of expensive and time consuming chemical analysis. Furthermore, a correlative approach based on canonical correlation analysis was used to investigate the spectral regions that are mostly correlated to the characteristics of interest. The identification of these regions, which can be linked to the absorbance of the main functional chemical groups, is intended to provide a better understanding of the chemical structure of the substrate under investigation.     

Evaluation of apple texture with contact acoustic emission detector: A study on performance of calibration models

Performance of calibration models for evaluation of apples sensory texture with contact acoustic emission detector (CAED) was studied. For model evaluation and testing, 2500 apples of 19 cultivars were harvested over two seasons. Apples were stored at normal atmosphere (NA), controlled atmosphere (CA) for different periods or were treated with 1-methylcyclopropene (1-MCP) in order to obtain a high variability of texture and fruit maturity. Apples were tested simultaneously in two distinct laboratories. The models were created and validated on averaged values from 10 fruits using simple linear regression, multiple linear regression (MLR) and principal component regression (PCR). Performance statistics of the models were expressed in terms of determination coefficient (R²), root mean square errors of cross validation (RMSECV) or prediction (RMSEP) and ratio of prediction to deviation (RPD). Firmness and total acoustic emission counts were predictors of sensory texture in the models. MLR and PCR models show better performance for prediction of sensory data than simple linear regression models however PCR models show the best results among models tested in this study. Common PCR models for several cultivars allow for successful prediction of hardness (RPD > 2.0), crispness and overall texture (1.5 < RPD < 2.0). The single-cultivar PCR models, constructed on data sets containing 26-39 averaged values, reveal significantly better performance (RPD > 2.0 for most of the cases) than the common PCR models for many varieties.