Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands

The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.

Near-Infrared Spectroscopy Coupled with Kernel Partial Least Squares-Discriminant Analysis for Rapid Screening Water Containing Malathion

Near-infrared spectroscopy coupled with kernel partial least squares-discriminant analysis was used to rapidly screen water containing malathion. In the wavenumber of 4348 cm-1 to 9091 cm-1, the overall correct classification rate of kernel partial least squares-discriminant analysis was 100% for training set, and 100% for test set, with the lowest concentration detected malathion residues in water being 1 μg·ml-1. Kernel partial least squares-discriminant analysis was able to have a good performance in classifying data in nonlinear systems. It was inferred that Near-infrared spectroscopy coupled with the kernel partial least squares-discriminant analysis had a potential in rapid screening other pesticide residues in water.

Discriminant Analysis of Liquor Brands Based on Moving-Window Waveband Screening Using Near-Infrared Spectroscopy

Partial least squares discriminant analysis (PLS-DA) with integrated moving-window (MW) waveband screening was applied to the discriminant analysis of liquor brands with near-infrared (NIR) spectroscopy. Luzhou Laojiao, a popular liquor with strong fragrant flavor, was used as the identified liquor brand (160 samples, negative, 52 vol alcoholicity). Liquors of 10 other brands with strong fragrant flavor were used as the interferential brands (200 samples, positive, 52 vol alcoholicity). The Kennard-Stone algorithm was used for the division of modeling samples to achieve uniformity and representativeness. Based on the MW-PLS-DA, a simplified optimal model set with 157 wavebands was further proposed. This set contained five types of wavebands corresponding to the NIR absorption bands of water, ethanol, and other micronutrients (i.e., acids, aldehydes, phenols, and aromatic compounds) in liquor for practical choice. Using five selected simple models with 4775 - 4239, 7804 - 6569, 6264 - 5844, 9435 - 7896, and 12066 - 10373 cm-1, the validation recognition rates were obtained as 99.3% or higher. Results show good prediction performance and low model complexity, and also provided a valuable reference for designing small dedicated instruments. The proposed method is a promising tool for large-scale inspection of liquor food safety.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Brand Identification of Wine

High-end wine brand is made through the use of high-quality grape variety and yeast strain, and through a unique process. Not only is it rich in nutrients, but also it has a unique taste and a fragrant scent. Brand identification of wine is difficult and complex because of high similarity. In this paper, visible and near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was used to explore the feasibility of wine brand identification. Chilean Aoyo wine (2016 vintage) was selected as the identification brand (negative, 100 samples), and various other brands of wine were used as interference brands (positive, 373 samples). Samples of each type were randomly divided into the calibration, prediction and validation sets. For comparison, the PLS-DA models were established in three independent and two complex wavebands of visible (400 - 780 nm), short-NIR (780 - 1100 nm), long-NIR (1100 - 2498 nm), whole NIR (780 - 2498 nm) and whole scanning (400 - 2498 nm). In independent validation, the five models all achieved good discriminant effects. Among them, the visible region model achieved the best effect. The recognition-accuracy rates in validation of negative, positive and total samples achieved 100%, 95.6% and 97.5%, respectively. The results indicated the feasibility of wine brand identification with Vis-NIR spectroscopy.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Identification of Soy Sauce Adulteration

The identification of soy sauce adulteration can avoid fraud, and protect the rights and interests of producers and consumers. Based on two measurement models (1 mm, 10 mm), the visible and near-infrared (Vis-NIR) spectroscopy combined with standard normal variate-partial least squares-discriminant analysis (SNV-PLS-DA) was used to establish the discriminant analysis models for adulterated and brewed soy sauces. Chubang soy sauce was selected as an identification brand (negative, 70). The adulteration samples (positive, 72) were prepared by mixing Chubang soy sauce and blended soy sauce with different adulteration rates. Among them, the “blended soy sauce” sample was concocted of salt water (NaCl), monosodium glutamate (C5H10NNaO5) and caramel color (C6H8O3). The rigorous calibration-prediction-validation sample design was adopted. For the case of 1 mm, five waveband models (visible, short-NIR, long-NIR, whole NIR and whole scanning regions) were established respectively;in the case of 10 mm, three waveband models (visible, short-NIR and visible-short-NIR regions) for unsaturated absorption were also established respectively. In independent validation, the models of all wavebands in the cases of 1 mm and 10 mm have achieved good discrimination effects. For the case of 1 mm, the visible model achieved the optimal validation effect, the validation recognition-accuracy rate (RARV) was 99.6%;while in the case of 10 mm, both the visible and visible-short-NIR models achieved the optimal validation effect (RARV = 100%). The detection method does not require reagents and is fast and simple, which is easy to promote the application. The results can provide valuable reference for designing small dedicated spectrometers with different measurement modals and different spectral regions.

基于PLS-DA和LS-SVM的可见/短波近红外光谱鉴定港种四九、十月红和九月鲜菜心种子的可行性研究

目前市面上菜心的品种复杂,不同菜心种子的品质与发芽率不同,但菜心种子单从外观上差别不大,因此区分菜心种子的类别成为了一大难题。为了实现菜心种子类别的快速区分,探究了基于可见/短波近红外光谱分析菜心种子类别的可行性。从南昌市种子交易场所购买了港种四九、十月红和九月鲜三个品种的菜心种子,从中挑选出品相较好且大小适中的子粒,将每种菜心种子均匀分为30份,按照2∶1划分为建模集和预测集,所有样本共计90份。通过近红外光谱仪获取采样间隔为1 nm的菜心种子的光谱反射率,波长覆盖范围325~1075 nm,将原始光谱数据采用多元散射校正(MSC)、卷积平滑(S-G)和标准正态变换(SNV)三种预处理方法进行预处理,预处理后的光谱变量建立偏最小二乘回归(PLSR)模型,确定了SNV是最佳预处理方法。采用主成分分析(PCA)对菜心种子进行了聚类分析,从前三个主成分因子(PCs)得分图可知三种菜心种子存在光谱特征差异。将原始光谱变量、前三个PCs(累计贡献97.15%)和基于随机蛙跳(RF)算法挑选的13个特征波长作为偏最小二乘判别(PLS-DA)和最小二乘支持向量机(LS-SVM)模型的输入变量,从模型结果可知:三种输入变量中,采用RF筛选特征波长作为模型输入变量时,模型预测效果最好,PCs建立的模型最差,相比于PCA分析,采用RF筛选出的特征波长更能够反映原始光谱信息。比较不同模型预测效果,LS-SVM模型比PLS-DA模型得到的预测精度更好,其中RF-LS-SVM模型是所有模型中最佳的预测模型,建模集和预测集均为100%。采用可见/短波近红外光谱研究菜心种子的类别可行,并且能够获得很好地预测效果,为菜心种子的快速区分提供了理论依据。

Rapid recognition of Chinese herbal pieces of Areca catechu by different concocted processes using Fourier transform mid-infrared and near-infrared spectroscopy combined with partial least-squares discriminant analysis

Rapid and sensitive recognition of herbal pieces according to different concocted processing is crucial to quality control and pharmaceutical effect. Near-infrared （NIR） and mid-infrared （MIR） technology combined with supervised pattern recognition based on partial least-squares discriminant analysis （PLSDA） was attempted to classify and recognize six different concocted processing pieces of 600 Areca catechu L. samples and the influence of fingerprint information preprocessing methods on recognition performance was also investigated in this work. Recognition rates of 99.24%, 100% and 99.49% for original fingerprint, multiple scatter correct （MSC） fingerprint and second derivative （2nd derivative） fingerprint of NIR spectra were achieved by PLSDA models, respectively. Meanwhile, a perfect recognition rate of 100% was obtained for the above three fingerprint models of MIR spectra. In conclusion, PLSDA can rapidly and effectively extract otherness of fingerprint information from NIR and MIR spectra to identify different concocted herbal pieces ofA. catechu.

基于microRNA表达谱初步构建PLS-DA体液识别模型

针对外周血、唾液、精液、月经血、阴道分泌物这五种法医学常见的体液样本,采用二代测序(NGS)平台检测获得microRNA(miRNA)表达谱,使用偏最小二乘判别分析(PLS-DA)建立基于这五种体液的识别模型,并探讨PLS-DA在法医体液溯源中的应用价值。经优化小分子RNA文库制备过程,采用Ion Torrent S5 XL测序系统对前述法医学五种体液样本(每种10例)进行小RNA测序,以PLS-DA构建体液识别模型,评估不同数量miRNA标记组合下预测的准确性。本研究获得法医学常见五种体液样本的miRNA表达谱,外周血与月经血中表达量前10名的miRNAs有6个重叠;唾液和阴道分泌物中表达量前10名的miRNAs有4个重叠。基于全数据集、107个和11个miRNAs构建的体液来源识别模型的准确率分别为0.95、0.94、0.89。本研究通过NGS测序分析获得了五种体液样本的miRNA组(miRNome),利用PLS-DA初步构建了体液识别模型,对于应用miRNome进行体液识别的相关研究具有参考价值。

基于HS-SPME-GC-MS和PLS-DA分析不同季节早白尖红茶挥发性风味物质

采集四川省筠连县春、夏、秋三季共15份早白尖红茶样品,采用顶空固相萃取-气相色谱-质谱技术对红茶样品的香气成分进行测定,运用偏最小二乘-判别分析(partial least square-discriminant analysis,PLS-DA)建立不同季节茶叶判别模型,绘制层次聚类的树状热图确定关键香气成分在不同季节样品中的分布规律。结果表明,春季样品醇类(113.05μg/g)和酯类物质(34.92μg/g)含量明显高于夏秋两季样品,而醛类物质(23.85μg/g)明显低于夏秋两季样品,且所建PLS-DA模型可将春和夏秋两季样品明显区分。进一步分析后的分层聚类的树状热图显示,苯乙醛、橙花醇和香叶醇是春季样品区别于其它两季茶样的特征香气化合物,在此基础上可通过芳樟醇、芳樟醇氧化物Ⅰ和Ⅱ对夏、秋两季样品进行进一步区分。该研究为解析不同季节早白尖红茶香气物质提供基础研究数据,也为进一步探究筠连早白尖红茶关键香气形成机制奠定基础。

Functional Data Analysis of Spectroscopic Data with Application to Classification of Colon Polyps

In this study, two functional logistic regression models with functional principal component basis (FPCA) and functional partial least squares basis (FPLS) have been developed to distinguish precancerous adenomatous polyps from hyperplastic polyps for the purpose of classification and interpretation. The classification performances of the two functional models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The results indicated that classification abilities of FPCA and FPLS models outperformed those of the PCDA and PLSDA models by using a small number of functional basis components. With substantial reduction in model complexity and improvement of classification accuracy, it is particularly helpful for interpretation of the complex spectral features related to precancerous colon polyps.

Functional Analysis of Chemometric Data

The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.

黄花败酱HPLC多组分定量控制及PCA、OPLS-DA联合GRA法综合质量评价

目的建立HPLC法同时测定黄花败酱中常春藤皂苷元、齐墩果酸、熊果酸、黄花败酱皂苷C、原儿茶酸、绿原酸、咖啡酸、秦皮乙素、东莨菪内酯、芦丁、槲皮素和山奈酚的含量,探索主成分分析(PCA)、正交偏最小二乘法-判别分析(OPLSDA)及灰色关联度分析(GRA)模型在黄花败酱质量评价中的应用。方法采用HPLC法,色谱柱为Waters Atlantis T3 C_(18)柱(250 mm×4.6 mm,5μm),以乙腈-0.1%磷酸为流动相,梯度洗脱;检测波长为210,260,360 nm;通过对多指标成分含量结果进行PCA、OPLS-DA和GRA分析,综合评价黄花败酱的整体质量。结果外标法方法学验证结果均符合要求;12种成分在各自范围内线性关系良好(r>0.9990),平均加样回收率在96.82%~100.16%之间(RSD<2.0%,n=9)。PCA、OPLS-DA结果显示齐墩果酸、熊果酸、绿原酸和槲皮素是影响黄花败酱产品质量的主要潜在标志物。GRA结果显示相对关联度在0.3806~0.5714之间,黄花败酱存在一定批间差异。结论HPLC法同时测定黄花败酱质量中12个成分,方法简便实用。PCA、OPLS-DA和GRA可用于评价黄花败酱的整体质量。

红芪搓条前后主要次级代谢产物变化规律研究

目的 考察红芪搓条前后主要次级代谢产物的变化规律。方法 UPLC-MS/MS法测定芒柄花素、芒柄花苷、毛蕊异黄酮、毛蕊异黄酮苷、美迪紫檀素、染料木素、木犀草素、甘草素、异甘草素、香草酸、阿魏酸、γ-氨基丁酸、腺苷、甜菜碱的含量,聚类分析、主成分分析、正交偏最小二乘判别分析进行化学模式识别以寻找差异性成分。结果 搓条后,芒柄花素、毛蕊异黄酮、甘草素、γ-氨基丁酸含量升高,芒柄花苷、毛蕊异黄酮苷、香草酸含量降低。搓条、未搓条药材聚为2类,毛蕊异黄酮苷、芒柄花素、γ-氨基丁酸、香草酸、毛蕊异黄酮、芒柄花苷为差异性成分。结论 本实验阐明红芪搓条前后化学成分差异,可为其他药材搓条机制研究提供参考。

基于电子鼻和气质联用分析萌芽大蒜挥发性物质差异

为探究萌芽期大蒜挥发性物质的差异,采用电子鼻、捕集阱顶空-气质联用仪(Trap head space-gas chromatography-mass spectrometry,HS-Trap-GC-MS)结合正交偏最小二乘法判别分析(Orthogonal partial least squares discriminant analysis,OPLS-DA)、香气活度值、差异性热图、相关性分析分析大蒜萌芽在0、24、48、72、96 h挥发性物质的差异。电子鼻结合OPLS-DA建立预测模型其预测能力达96.00%。GC-MS分析表明:含硫化合物是不同萌芽期大蒜的主要共有挥发性物质,含硫化合物的相对含量随萌芽时间的延长而呈递减趋势,而种类呈现出递增趋势;二烯丙基二硫醚是样品在萌芽过程中含量降低最多的物质。二烯丙基四硫醚、烯丙硫醇是样品共有关键化合物。差异性热图分析显示:除共有物质含量差异外,硫化丙烯、己醛、叠氮二羧酸二叔丁酯、丙烯醇、6-甲基-2-庚炔、5-甲基噻二唑、2-亚乙基-1,3-二硫烷、2-丙-2-炔基磺酰基丙烷、2,5-二甲基噻吩、2,5-二甲基呋喃、1-戊烯-3-醇、1,3-二噻烷的缺失进一步加大了未萌芽和萌芽大蒜气味的差异。萌芽大蒜主要共有挥发性物质的种类随萌芽时间的延长呈现递增趋势。大蒜主要挥发性物质与电子鼻大多数传感器存在显著相关性。大蒜的气味强度会随萌芽时间的延长而逐步减弱。

基于电子鼻、HS-SPME-GC-MS和HS-GC-IMS评价不同制油工艺对大豆油品质及风味的影响

为探究不同制油工艺(冷榨法、浸出法、冷榨-浸出法)对三级大豆油品质及风味的影响,测定了大豆油的理化性质及脂肪酸组成,分别采用电子鼻、顶空-固相微萃取-气相色谱-质谱(headspace-solid phase microextraction-gaschromatography-massspectrometry,HS-SPME-GC-MS)法和顶空-气相色谱-离子迁移谱(headspace-gas chromatography-ion mobility spectroscopy,HS-GC-IMS)法鉴定3种豆油中挥发性化合物,并借助聚类热图、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3种豆油的挥发性化合物数据进行差异分析。结果表明,冷榨型豆油的水分含量最低,浸出型豆油过氧化值显著偏高且油脂色泽最深;浓香型豆油中亚油酸含量最多,营养价值更高;被检出的挥发性组分中,醇类、醛类以及吡嗪类化合物为豆油风味的形成做出主要贡献,明晰了部分风味化合物形成的原因。最终通过OPLS-DA筛选出45种贡献较大的挥发性化合物,同时构建可靠的用以鉴别浓香型豆油的模型。此外,发现豆油的品质与风味之间存在一定的相关性。

十里香夏季花香型红茶加工工艺优化及其品质分析

为优化十里香夏季花香型红茶的加工工艺,提升其品质,该研究采用感官审评、化学品质分析、聚类分析和偏最小二乘判别分析(partial least squares discrimination analysis,PLS⁃DA)对不同工艺加工的茶样品质进行综合评价。结果表明,16个工艺加工的十里香夏季花香型红茶的感官品质得分为88.3~93.8,其中有8个产品的得分超过90,整体表现较优;水浸出物、茶多酚、游离氨基酸、茶黄素、茶红素、茶褐素、咖啡碱含量以及儿茶素总量范围分别为30.39%~43.38%、9.69%~22.68%、1.55%~2.96%、0.06%~0.66%、0.86%~6.71%、0.29%~8.93%、5.18%~5.96%、5.79%~12.72%;系统聚类以欧式平方距离为15时可将16个茶样聚为3大类;PLS⁃DA可通过品质成分得分图、拟合曲线、载荷图及其变异权重参数预测型将不同茶样进行区分。十里香夏季花香型红茶的最优加工工艺为萎凋时间12 h、发酵时间2 h、足火温度80℃。

PLS-DA法判别分析木材生物腐朽的研究

利用近红外光谱结合PLS-DA判别分析方法可用于食品、药品和农产品等的快速识别或检测,因此,研究利用近红外光谱结合PLS-DA方法来检测木材的生物腐朽。研究结果表明:应用近红外光谱结合PLS-DA方法对培训集样本建立的判别模型,其校正及验证结果与实际分类变量的相关系数均超过0.94,SEC和SEP都低于0.17;利用模型对未参与建模的样本进行检测,发现该模型对未腐朽、白腐和褐腐三种类型样本的判别准确率均为100%(偏差均小于0.5);与SIMCA法相比,PLS-DA法对木材生物腐朽样本的判别准确率更高,说明应用近红外光谱结合PLS-DA方法能快速地检测到木材的生物腐朽,并能准确地判别出木材的生物腐朽类型。

五种产香酵母对发酵耙豌豆挥发性风味物质的影响

为探究产香酵母菌对耙豌豆挥发性风味物质的影响,该文用5种酵母(安琪酵母、季也蒙毕赤酵母、酿酒酵母、鲁氏酵母和近平滑假丝酵母)对耙豌豆进行固态发酵5 d。采用电子鼻结合GC-MS对发酵耙豌豆挥发性风味物质进行鉴定分析,通过主成分分析和偏最小二乘-判别分析(partial least square-discriminant analysis,PLS-DA)对不同酵母菌发酵耙豌豆的香气进行差异分析。电子鼻分析表明酵母菌发酵对耙豌豆挥发性风味有影响,酵母发酵耙豌豆与未发酵耙豌豆整体风味差异较大。GC-MS共检测出69种挥发性风味物质,系以醇类和酸类为主,其次为酯类和酮类。5种不同酵母发酵耙豌豆的挥发性香气成分差异显著(P<0.05),假丝酵母发酵耙豌豆样品中的挥发性风味物质种类最丰富,含38种。PLS-DA模型筛选得到12种关键风味物质(VIP>1),主要是异丙醇、异戊醇、2-甲基丁醇和乙酸乙酯等物质,赋予发酵耙豌豆醇香、果香和麦芽香。因此,产香酵母能降低耙豌豆的豆腥味,显著改善耙豌豆的风味。综上,该研究为酵母发酵耙豌豆工业化生产提供数据支持,为开发发酵型耙豌豆产品提供理论基础。

香附饮片与4种醋制香附饮片的快速鉴别

目的建立香附饮片与4种醋制香附饮片的傅里叶变换红外光谱图,快速鉴别香附饮片与4种醋制香附饮片;建立香附饮片与4种醋制香附饮片的高效液相指纹图谱,并测定α-香附酮、香附烯酮、5-羟甲基糠醛(5-HMF)、对香豆酸、阿魏酸、木犀草素的含量。方法建立25批香附样品的红外光谱图,利用OMNIC 9.2软件分析其平均红外光谱间的差异,采用Spectrum for Window 3.02软件标定共有峰,计算相对峰高;采用《中药色谱指纹图谱相似度评价系统(2012版)》建立香附饮片与4种醋制香附饮片的高效液相指纹图谱,进行相似度评价,确定共有峰个数;采用SIMCA 14.1软件进行聚类分析、主成分分析、正交偏最小二乘法-判别分析,判断红外光谱图结果与高效液相指纹图谱结果是否相互验证;测定α-香附酮、香附烯酮、5-HMF、对香豆酸、阿魏酸、木犀草素的含量。结果25批香附样品的红外光谱图的相关系数为0.9347~0.9829,共标定16个共有峰,1500~1300 cm^(-1)波段处差异明显。正态分布分析结果显示,2928、1649、995 cm^(-1)波段处可分别将醋煮品、醋炙品、醋蒸品与饮片区分;高效液相指纹图谱结果显示25批样品共有21个共有峰,相似度均大于0.9。指认出3号峰为5-HMF,7号峰为对香豆酸,8号峰为阿魏酸,14号峰为木犀草素,19号峰为香附烯酮,21号峰为α-香附酮;红外光谱图与高效液相指纹图谱聚类分析结果、主成分分析结果与正交偏最小二乘法-判别分析结果一致,均可聚为五类,即能够相互验证;5-HMF的含量经炮制后均有增加。对香豆酸、阿魏酸、木犀草素在饮片中含量最低,经炮制后均有增加。香附烯酮经醋炙、醋煮、醋蒸、醋煮蒸后均有减少,在香附饮片中含量最高。α-香附酮经醋炙后含量略有增加,经醋煮、醋蒸、醋煮蒸后均减少。结论香附饮片与4种醋制香附饮片的红外光谱图之间存在明显差异,能够快速鉴别香附饮片与4种醋制香附饮片;所建立的高效液相指纹图谱与含量测定方法操作简单、准确。

基于主成分分析的冰温气调包装对松露的品质指标及相关性影响

目的:探究冰温技术结合O_(2)/CO_(2)气调包装技术对松露贮藏期间相关品质指标的影响。方法:以松露为原料,分别在-(4.4±0.2)℃的冰温条件下(CK组)和冰温结合40%O_(2)+60%CO_(2)气调条件下(A组)进行贮藏,测定其贮藏期间各项品质指标的变化。采用主成分分析和正交偏最小二乘判别分析建立判别模型。结果:经两种贮藏方式的松露各项指标呈现出不同的变化规律。A组松露保鲜效果明显优于单一的冰温贮藏,能够有效减少水分流失和腐烂现象,在贮藏第20天时,A组松露水分质量分数、腐烂率和质量损失率分别为63.62%、6.94%和1.02%,前者显著高于同时期的冰温对照组(P<0.05),后两者比CK组低(P<0.05)。A组能更好地维持松露品质的稳定,在保持硬度和弹性等质构特性及延缓多糖、总多酚、总黄酮、粗蛋白含量、铁离子还原能力下降方面都有较好效果。在贮藏第20天时,A组多糖、总多酚、总黄酮、粗蛋白含量、铁离子还原能力值与0?d相比时分别降低了27.94%、32.51%、16.18%、68.58%、18.13%,降幅均低于CK组。通过相关性分析和构建判别模型,能够有效区分样品在两种不同处理下的品质差异,说明在贮藏期间理化指标对松露的品质存在一定的影响;正交偏最小二乘判别分析能有效区分不同处理的组分。结论:A组包装对新鲜松露有更好的贮藏保鲜效果,本研究为松露保鲜技术开发提供理论参考。