基于近红外光谱与PLS-DA的红枣品种识别研究

新疆红枣品种繁多,采后红枣在加工过程中需要将其他品种的红枣挑选出,本研究应用近红外光谱分析技术结合偏最小二乘判别分析（PLS-DA）法对新疆红枣品种进行判别。结果表明,采用一阶导数对原始光谱进行预处理,并使用方差分析法选择波长变量结合PLS-DA方法对校正样本建立判别分析模型,其验证集预测结果与实际分类结果的相关系数（RP）均大于0.92,预测标准偏差（RMSEP）都小于0.27,最后模型对验证集中的骏枣、灰枣和冬枣3个品种的识别率都为100%。该结果为新疆红枣品种快速识别提供理论依据。      

Detection of expired vacuum-packed smoked salmon based on PLS-DA method using hyperspectral images

Consumers want fresh food with a long shelf-life, which in 2010, resulted in an important increase in packaged and processed food. This is especially important for fishery products due to their highly perishable nature. One problem is that it is not possible to measure freshness in packaged food only using the visible spectrum. Moreover, the detection of freshness is a complex problem as fish has different tissues with different biodegradation processes. Therefore, it would be especially interesting to have a non-destructive method to evaluate the shelf-life of packed processed fish. This paper proposes a method for detecting expired packaged salmon. Firstly, this method uses hyperspectral imaging spectroscopy (HIS) using visible and SW-NIR wavelengths. Secondly, a classification of different salmon tissues is carried out by image segmentation. Finally, classifications of expired or non expired salmon are performed with the PLS-DA statistical multivariate method due to the large amount of captured data. In a similar way, spectral data and the physicochemical, biochemical and microbiological properties of salmon are correlated using partial least squares (PLSs). The result obtained has a classification success rate of 82.7% in cross-validation from real commercial samples of salmon. Therefore, this is a promising technique for the non-destructive detection of expired packaged smoked salmon.     

基于质谱与化学计量学的浓香型白酒等级鉴别

不同等级白酒的鉴别对控制白酒质量和保护消费者权益有重要意义,运用顶空固相微萃取质谱(HS-SPME-MS)技术获取3个不同等级的120个洋河大曲酒样质荷比m/z 55～191范围内的离子丰度值数据,结合偏最小二乘-判别分析和逐步线性判别分析法筛选出14个重要特征离子,且交叉验证的预测准确率达100%;然后将筛选出的14个特征离子作网络输入层,酒样的不同等级做网络输出层,构建神经网络等级鉴别模型,其在±0.3的误差范围内,预测准确率达100%,实现了白酒等级的数字化鉴别。     

标准化工艺条件下大河乌猪火腿发酵过程中的挥发性风味物质变化

该研究采用固相微萃取-气相色谱-质谱（solid-phase microextraction-gas chromatography-mass spectrometry,SPME-GC-MS）技术结合偏最小二乘－判别分析（PLS-DA）和相对气味活度值（relative odor activity value,ROAV）对发酵90 d、150 d、210 d、270 d、450 d和630 d的大河乌猪火腿的挥发性风味物质进行分析。结果表明：发酵过程中共鉴定到137种挥发性风味成分,包括醛、醇、酸、烃、酮、酯、芳香族类及其他类化合物,其中,醛类（51.63%~68.17%）和醇类（12.73%~23.64%）的种类和含量最为丰富;随着发酵时间的延长,挥发性风味物质种类增加了23种,但四个不同发酵期（210 d、270 d、450 d、630 d）的大河乌猪火腿中挥发性风味物质组成相似,说明发酵至210 d时主体风味化合物已经形成;发酵过程中关键风味物质有1-庚醇、3-甲基-1-丁醇、3-甲基丁醛、2,3-辛二酮、苯乙醛、己醛、辛醛、壬醛等16种。标准化工艺条件下发酵时间对火腿挥发性风味物质的形成有一定的影响,且大河乌猪火腿的加工期可定为10~12个月,研究为大河乌猪火腿标准化加工技术提供理论依据。     

基于GC-IMS技术的浓香型白酒等级判别方法

为探究不同等级浓香型白酒挥发性物质差异及其贡献程度,采用感官评价、气相色谱-离子迁移谱技术（GC-IMS）结合化学计量学方法对不同等级浓香型白酒的挥发性物质进行分析。经GC-IMS检测可筛选出41个能够表征3个等级白酒挥发性物质差异的变量,包括酯类13种、醇类8种、酮类6种、醛类3种以及其它类化合物4种。基于41个变量的峰体积进行主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）,结果显示,PCA前2个主成分累计贡献率达到80.1%,可有效区分不同等级的酒样。PLS-DA依据变量重要性投影,可筛选出17种特征性标志物。对以上物质绘制聚类热图,区分香气物质对不同酒样的贡献程度,并构建K近邻模型（KNN）。当K为5时,判别准确率达100%。此研究方法可为浓香型白酒等级划分的快速评估提供理论依据。     

杀菌温度对乳化肠中脂肪酸组成和脂肪氧化的影响

为明确脂肪对不同熟化温度下（80 ℃,30 min、90 ℃,30 min、100 ℃,30 min和121 ℃,20 min）猪肉乳化香肠挥发性物质的影响,采用感官评价、电子鼻（Electronic nose,E-nose）和固相微萃取-气相色谱质谱联用技术（Solid-phase microextraction coupled-gas chromatography-mass spectrometry,SPME-GC-MS）对添加或不添加脂肪的乳化肠在不同熟化温度下挥发性物质进行分析。结果表明,添加脂肪的乳化肠,熟化条件为100 ℃,30 min的样品风味最好,添加脂肪可提高乳化肠脂香味、硫磺味、哈喇味和青草味的感官强度,抑制高温乳化肠肉香味和蘑菇味的感官强度;电子鼻能有效区分添加脂肪或不添加脂肪的样品,但80 ℃,30 min组的样品无法有效区分;添加脂肪的样品中,除100 ℃,30 min组和121 ℃,20 min组外,电子鼻能很好的将不同熟化温度的样品区分开。SPME-GC-MS结果显示,8个处理组共检测出56种挥发性化合物,己醛、庚醛、辛醛、戊醛、壬醛、1-辛烯、1-辛烯-3-醇和甲硫醇等关键化合物的含量随熟化温度的升高而增加。偏最小二乘判别分析（Partial least squares discriminant analysis,PLS-DA）筛选出己醛、戊醛和n-己酸乙烯酯是不同熟化温度乳化肠气味差异的潜在标志物。采用正交偏最小二乘判别分析（Orthogonal partial least squares discriminant analysis,OPLS-DA）筛选出添加与不添加脂肪的样品在4种熟化温度下的差异化合物分别为17种、17种、22种和25种。以上结果表明,熟化条件相同,添加脂肪的样品挥发性物质含量显著高于不添加脂肪的样品,100 ℃,30 min的熟化条件更有利于乳化肠风味的形成。

     

苹果品种及损伤苹果的FT-NIR鉴别研究

用傅里叶近红外光谱技术(FT-NIR)对不同品种的苹果以及损伤嘎啦和完好嘎啦进行快速、无损检测,比较不同判别方法对所建立的区分苹果品种及苹果损伤模型的影响。结果表明:损伤嘎啦和完好嘎啦的近红外图谱经小波分析预处理后,用12000～4000cm-1波数范围的前5个主成分分别结合多层感知神经网络、径向基神经网络、Fisher判别3种方法所建立的判别模型对未知样本的正确判别率分别为97.8%、87.2%和84.8%,基于权重法用多元线性回归(MLR)所选择的特征波长所建立的Fisher判别模型对未知样本的正确判别率为89.1%;用偏最小二乘判别(PLS-DA)所建立的判别模型对未知样本的正确判别率为100%,由于PLS-DA模型对训练集和验证集的正确判别率均为100%,因此PLS-DA模型优于其他模型。不同品种苹果的光谱经平滑预处理后,用全波数范围12000～4000cm-1的前6个主成分所建立的判别模型优于经验波数范围8000～4500cm-1所建立的判别模型,其较优模型对建模集和验证集的正确判别率分别为90.9%和92.1%。近红外光谱技术结合化学计量学可以快速、无损鉴别苹果是否有损伤以及不同品种的苹果。     

普洱熟茶化学成分与品质特性相关性分析

以50个普洱熟茶为研究对象,采用主成分分析和偏最小二乘回归分析法,研究11个化学成分和感官审评结果间的相关性,揭示影响品质特性的主要因子。结果表明,水浸出物、总糖、茶多糖、茶多酚、氨基酸和茶色素与品质呈正相关;水分、总灰分、pH值、粗纤维和咖啡碱与品质呈负相关。     

Applications of FT-NIRS combined with PLS multivariate methods for the detection & quantification of saccharin adulteration in commercial fruit juices

Detection of adulteration in carbohydrate-rich foods like fruit juices is particularly difficult because of the variety of the commercial sweeteners available that match the concentration profiles of the major carbohydrates in the foods. In present study, a new sensitive and robust assay using Fourier Transform Near-Infrared Spectroscopy (FT-NIRS) combined with partial least square (PLS) multivariate methods has been developed for detection and quantification of saccharin adulteration in different commercial fruit juice samples. For this investigation, six different commercially available fruit juice samples were intentionally adulterated with saccharin at the following percentage levels: 0%, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, 1.50%, 1.70% and 2.00% (weight/volume). Altogether, 198 samples were used including 18 pure juice samples (unadulterated) and 180 juice samples adulterated with saccharin. PLS multivariate methods including partial least-squares discriminant analysis (PLS-DA) and partial least-squares regressions (PLSR) were applied to the obtained spectral data to build models. The PLS-DA model was employed to differentiate between pure fruit juice samples and those adulterated with saccharin. The R² value obtained for the PLS-DA model was 97.90% with an RMSE error of 0.67%. Similarly, a PLS regression model was also developed to quantify the amount of saccharin adulterant in juice samples. The R² value obtained for the PLSR model was 97.04% with RMSECV error of 0.88%. The employed model was then cross-validated by using a test set which included 30% of the total adulterated juice samples. The excellent performance of the model was proved by the low root mean squared error of prediction value of 0.92% and the high correlation factor of 0.97. This newly developed method is robust, nondestructive, highly sensitive and economical.