近红外漫反射光谱技术对小米产地溯源的研究

为建立小米产地溯源的快速检测技术,更好的维护地方名优小米品牌效益,试验利用近红外漫反射光谱技术对不同状态小米进行产地溯源鉴别,试验分别选取来自肇源和肇州两个小米主产区的144份小米样品,应用近红外漫反射光谱技术结合化学计量学对不同状态下的小米进行产地溯源研究,结果表明:在全波长范围内采用因子化法建立的定性分析模型和在特征波段范围内采用偏最小二乘法(PLS)建立的定量分析模型,对肇源、肇州两个小米主产区的小米籽粒和小米粉末的正确鉴别率均在90%以上,其中小米粉末的模型正确预测率要高于小米籽粒。因此,应用近红外漫反射光谱技术对不同状态小米产地溯源的鉴别具有一定的可行性。     

基于近红外光谱技术的茶叶新旧鉴别及产地溯源研究

摘 要：目的 建立基于近红外光谱的定性分析模型,实现对茶叶的新旧分类和产地溯源。方法 首先采用傅里叶近红外光谱仪采集茶叶样品的漫反射光谱数据,然后使用SG平滑算法（savitzky-golay smoothing, SG）和数据标准化（normalization）对光谱数据进行预处理,最后基于遗传优化算法（genetic algorithem, GA）和粒子群优化算法（particle swarm optimization, PSO）分别建立了优化向量机模型（support vector machine, SVM）,从而实现新旧茶叶的分类以及产地溯源。结果 与GA-SVM模型相比,PSO-SVM模型的建模效果较好,且分类时间更短,在新旧鉴别和产地溯源实验中都达到了100%的预测精度。结论 基于近红外光谱建立的PSO-SVM模型可以实现茶叶新旧的判别以及产地溯源,为鉴别茶叶年份和追踪茶叶产地提供了理论支撑和技术指导。     

近红外漫反射光谱技术对小米产地的快速检测

为实现对小米产地溯源的快速检测,保护优质小米的品牌效益,维护消费者的合法权益,采用近红外漫反射光谱技术结合化学计量学,对来自肇源及肇州两地区的144份小米样品进行产地溯源研究。结果表明,采用因子化法建立的鉴别分析模型对肇源及肇州地区的小米样品鉴别正确率均为100%;采用欧氏距离法建立的聚类分析模型对两地区小米样品的鉴别正确率分别为95.65%和100%;采用偏最小二乘法建立的定量分析模型对两地区小米样品的鉴别正确率分别为95.65%和91.67%。由此可知,近红外漫反射光谱法是一种快速、高效、无损的小米产地溯源判别技术。     

近红外光谱技术鉴别地理标志产品黄骅冬枣

采用近红外光谱技术结合合格性测试、主成分分析聚类方法,建立了快速鏊别地理标志产品黄骅冬枣的模型.收集产地不同的冬枣,使用聚焦不旋转固体漫反射方法,设定分辨率16cm-1,扫描范围4000～12000cm-1,采集样品近红外光谱.每个产地随机选取45个枣果,其中30个用来建立模型,余下的15个用于预测.通过对预处理方法和光谱波段的选择,两种方法使用的光谱范围均为4952.7～5693.2cm-1和6611.3～7537cm-1.原始光谱经矢量归一化预处理后进行合格性测试分析,建立黄骅冬枣的鉴别模型,预测准确率93.3%;经一阶导数+矢量归一化,17点移动式平均平滑预处理后,采用主成分分析(PCA)法对光谱进行聚类,预测准确率93.3%.两种方法均可作为快速无损地鉴别真伪黄骅冬枣的技术依据.     

近红外光谱法快速鉴别涪陵榨菜品牌的研究

提出了一种基于近红外漫反射光谱技术快速鉴别涪陵榨菜品牌的方法。应用近红外漫反射光谱技术对市场上6种典型品牌涪陵榨菜提取光谱,并对获得的原始光谱数据进行平滑、一阶导数以及矢量归一化等预处理后,采用因子法计算样品间的光谱距离,通过ward’salgorithm方法进行聚类分析。并与判别分析的实验结果进行了对比,结果显示聚类分析和判别分析对6种涪陵榨菜品牌均能很好地鉴别。     

酸奶中非脂乳固体的近红外定量分析模型建立

采用偏最小二乘回归PLS建模算法,建立酸奶中非脂乳固体的近红外定量分析模型,并对模型进行验证评估。收集92组酸奶样品,并用漫反射方法采集得到近红外扫描光谱,光谱经过MSC、一阶导数、S-G平滑等预处理,选取波数范围6 000~10 000 cm-1,用PLS法建立得到了较优模型,其相关系数R为0.99078,均方根校正误差RMSEC为0.152,均方根预测误差RMSEP为0.330,性能指数PI为83.1。用此模型对25组酸奶样品进行了预测,预测效果较好。     

基于近红外光谱的掺伪花生油鉴别模型优化

为实现掺伪花生油的快速鉴别,基于近红外光谱技术,利用偏最小二乘法(PLS法)建立掺伪花生油鉴别模型,并采用不同预处理方法进行模型优化。研究结果表明:利用PLS法建立的模型,对于花生油中掺入大豆油样品的鉴别,在漫反射方式下、采用二阶导数谱、Norris平滑方法、附加散射矫正光程方式、因子数为6时最为理想,其预测集相关系数为0.967 9;对于花生油中掺入菜籽油样品的鉴别,在漫反射方式下、采用二阶导数谱、S-G平滑方法、光程不矫正、因子数为5时最为理想,其预测集相关系数为0.994 8。该分析模型可以为花生油品质监控和快速定量鉴别掺伪提供参考。     

近红外光谱用于鉴别苹果产地的研究

目的:探索近红外光谱定性分析技术鉴别苹果产地的可行性。方法:对天津、陕西和北京3个产地富士苹果品质进行了分析,比较了波段范围、导数处理和散射及标准化处理建立主成分分析结合最小二乘法产地鉴别模型。结果:在1100~1848 nm波段范围内采用一阶导数结合趋势变化法散射处理的光谱预处理方法最优,校正集的鉴别正确率为100%,校正交互验证误差(SECV)0.1245,交互验证相关系数(Rcv)0.9641,预测集的鉴别正确率为98.33%。结论:应用近红外光谱定性分析技术可以准确地、快速地追溯苹果产地的溯源。     

基于近红外光谱技术有监督模式识别的青皮产地溯源分析

利用便携式近红外光谱仪采集不同产地(安徽、广东、四川)青皮外壁和内囊光谱数据,采用单一预处理和组合预处理方法消除光谱中的多种干扰,结合主成分分析(PCA)、簇类独立软模式分类法(SIMCA)及Fisher线性判别分析(FLDA)等模式识别方法建立青皮产地溯源模型。结果表明,光谱预处理可以在一定程度上消除基线漂移、背景噪声和谱峰重叠干扰,但无法实现产地溯源。3种模式识别方法中,PCA无法实现青皮产地溯源;青皮外壁和内囊原始光谱的SIMCA模型获得的青皮产地溯源整体鉴别率分别为99.14%和98.28%;FLDA模型获得的整体鉴别率均为99.57%,优于SIMCA模型;经光谱预处理优化后的SIMCA和FLDA模型对青皮产地溯源的鉴别率均可达100%,即便携式近红外光谱技术结合有监督模式识别方法可实现青皮产地溯源的无损分析,可为食药同源物质产地溯源拓展新途径。     

基于Fisher判别法对黑龙江大米产地溯源

为了探索大米产地鉴别可行性,维护市场秩序和消费者的合法权益,该研究采用Fisher判别法(Fisher discriminant method,费希尔判别法)进行建模并结合近红外光谱技术,对2015年黑龙江5个水稻主产区(五常、佳木斯、齐齐哈尔、双鸭山、牡丹江)的118份大米粉末样品进行近红外光谱的扫描,光谱预处理方法为9点二阶求导结合5点平滑,建模波长为全波长。对模型采用留一交叉验证和预测样本集进行验证,5个地域的验证结果分别为94.4%、94.4%、91.7%、91.7%、94.4%和87.5%、87.5%、87.5%、100%、100%。预测结果达到80%以上,初步认定近红外光谱指纹分析技术可用于黑龙江大米产地溯源。     

基于近红外光谱与矿物元素指纹信息融合对绿豆产地判别模型比较分析

为提高模型对绿豆产地的鉴别率,通过近红外光谱指纹信息和矿物元素指纹信息融合技术分析来自黑龙江省杜尔伯特蒙古自治县、吉林省白城市、黑龙江省泰来县、山东省泗水县绿豆样品中近红外光谱吸收强度和矿物元素含量,结合偏最小二乘判别分析(PLS-DA)法建立融合这两种指纹信息的鉴别方法。结果表明,信息融合模型的绿豆产地鉴别准确率为100%,与单一光谱指纹信息模型(90.0%)和矿物元素指纹信息模型(96.7%)相比,分别提高了10.0%和3.3%。因此,采用PLS-DA法信息融合模型对绿豆产地进行鉴别是可行的,近红外光谱指纹信息和矿物元素指纹信息融合技术可以提高绿豆产地的鉴别效果。     

近红外光谱的煎炸油羰基值检测及监控研究

为实现煎炸油煎炸过程中羰基值的快速检测和实时监控,采集不同混合比例、不同氧化程度的混合油及不同煎炸时间的煎炸油共108个样品的近红外光谱,分别采用一阶求导、二阶求导、Norris平滑、标准正交变换(SNV)及多元散射校正(MSC)对光谱进行预处理,偏最小二乘法(PLS)建立模型,对模型进行验证及利用模型对煎炸油煎炸过程中的羰基值进行监控。结果表明:在9 739~6 274 cm-1波数范围内采用SNV和二阶求导进行光谱预处理后PLS建模,其模型校正集和验证集的相关系数分别为0.984 4和0.979 0,模型可用;利用模型与传统方法同时测定煎炸油煎炸过程的羰基值,2种方法测定结果线性相关性良好(R2=0.995 2),模型可用于煎炸过程中羰基值的监控分析。     

基于近红外光谱技术的花生酸败特性检测研究

酸价、过氧化值是衡量花生贮藏过程中氧化酸败的重要指标,基于近红外光谱分析技术,结合化学计量分析,利用偏最小二乘法建立花生酸价、过氧化值的近红外预测模型,并对平滑、导数、多元散射校正、归一化等多种预处理方法对建模准确性的影响进行比较.结果表明:对原始光谱数据采用一阶微分处理的方法建立的模型其预测效果最佳,酸价模型的主成分维数为10,决定系数为0.955,均方根误差为0.080;过氧化值模型的主成分维数10,决定系数为0.940,均方根误差为0.459.研究表明,近红外光谱分析技术可用于花生酸价、过氧化值的快速无损检测.     

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.     

多毛刺小样本高光谱数据下鹰嘴蜜桃含水率的预估

目的：提出并解决鹰嘴蜜桃高光谱测量数据多毛刺和小样本问题。方法：基于高光谱成像技术，使用图像处理方法识别高光谱图像中鹰嘴蜜桃所在区域，计算该区域内的光谱图像从而得到平均光谱反射率数据，形成高光谱曲线图像。对于存在抖动和毛刺的高光谱图像数据，比较多项式平滑算法（SG）、多元散射矫正算法（MSC）、标准正态变量算法（SNV）、一阶导数算子（D1）、二阶导数算子（D2）等数据预处理方法对模型预测精度的影响；针对数据维度高且样本量少的特点，使用主成分分析算法（PCA）对数据进行降维，再对降维后的数据应用马氏距离测度方法（MD）进行异常值剔除；最终利用Kennard-Stone算法（KS）划分出训练集和测试集，并选取小样本场景下表现较好的偏最小二乘回归（PLSR）模型对鹰嘴蜜桃的含水率进行估计和分析。结果：SG-PCA-MD-KS-PLSR模型在高光谱曲线存在抖动和毛刺情况时对鹰嘴蜜桃含水率估计的效果最好，训练集下决定系数（R²）达到0.928，均方根误差（RMSE）为0.008 4，测试集下R²达到0.926，RMSE为0.009 2。在进一步对鹰嘴蜜桃以含水率为指标进行分级试验时，该模型的预测结果可以较好地对鹰嘴蜜桃含水状况进行分级，训练集下分级正确率为0.956，测试集下分级正确率为0.923。结论：利用高光谱成像技术建立SG-PCA-MD-KS-PLSR模型，在高光谱样本数较小且存在毛刺的情况下，仍能对鹰嘴蜜桃含水率进行无损估计。     

Rapid detection and quantification of soya bean oil and common sugar in bovine milk using attenuated total reflectance–fourier transform infrared spectroscopy

Attenuated total reflectance–Fourier transform infrared spectroscopy, along with chemometrics, were used to detect and quantify soya bean oil (SO) and sugar (CS) adulteration in milk. Bovine milk was artificially adulterated with SO (0.2–2.0%; v/v) and CS (1–10%; w/v) separately. Spectra revealed significant differences in specific wavenumber regions (SO: 1450–1250 cm^?1; CS: 1200–900 cm^?1). Soya bean oil adulteration was best predicted in wavenumber range of 1262–1164 cm^?1, using partial least square regression (coefficient of determination (R²: 0.90 and 0.88 for calibration and validation, respectively). Common sugar adulteration was best predicted in wavenumber range of 1010–910 cm^?1 (R²: 0.99 for calibration and validation) using partial least square.     

Predicting of intramuscular fat content in pork using near infrared spectroscopy and multivariate analysis

The potential of near infrared (NIR) spectroscopy combined with chemometrics methods was studied to rapidly detect intramuscular fat (IMF) content in pork. Near infrared diffuse reflectance spectra were recorded both with an FT-NIR and a USB4000 spectrometer. The data analysis was compared on different sample preparation, spectral range and spectra pretreatment. According to calibration statistics, best calibration for IMF showed R²_cal of 0.94, R²_val of 0.92, RMSEC of 0.233, RMSEP of 0.462 and RPD of 2.29. The prediction of IMF content for minced samples was more accurate than that for intact samples. The spectra obtained using FT-NIR contained much information correlating to the IMF content than the Vis-NIR spectra of USB4000. The results showed that NIR spectroscopy technique can be used to determine the IMF content in pork as a rapid, convenient, and feasible analysis tool.     

Inhibitory effect of mung bean extract and its constituents vitexin and isovitexin on the formation of advanced glycation endproducts

The anti-glycation activity of four kinds of beans including mung bean, black bean, soybean and cowpea were evaluated. Aqueous alcohol extract of mung bean exhibited the strongest inhibitory activity against the formation of fluorescent advanced glycation endproducts (AGEs) in a bovine serum albumin (BSA)-glucose model, and the inhibitory activities of extracts of the four beans were found to be highly correlated with their total phenolic contents (R² = 0.95). Subsequent HPLC analysis of mung bean extract revealed two major phenolics which were purified and identified as vitexin and isovitexin by spectral methods. In the anti-glycation assays, both vitexin and isovitexin showed significant inhibitory activities against the formation of AGEs induced by glucose or methylglyoxal with efficacies of over 85% at 100 μM. In another assay, vitexin and isovitexin failed to directly trap reactive carbonyl species, such as methylglyoxal, suggesting that their anti-glycation activities may mainly be due to their free radical scavenging capacity.     

Predicting cutting and clotting time of coagulating goat's milk using diffuse reflectance: effect of pH,temperature and enzyme concentration

A diffuse reflectance sensor, which used optical fibres and near-infrared radiation (NIR) at 880 nm, was used to monitor goat's milk coagulation. A randomised block design replicated three times was utilized to test the effects of pH, temperature and enzyme concentration on diffuse reflectance parameters. Milk pH was adjusted to three levels (5.5, 6.0 and 6.5) and coagulated at three different temperatures (28, 32 and 36°C), using three enzyme concentrations (0.020, 0.035 and 0.050 mL kg⁻¹ of milk). A linear cutting time prediction equation, T_cut=βT_max, was found to predict visual cutting time with a standard error of prediction of 84.5 s and an R² of 0.9785. β was affected by pH, temperature and enzyme concentration. The diffuse reflectance parameter (T_max) was strongly correlated to the Berridge clotting time (R²=0.9913). Parameters generated from the diffuse reflectance profiles, with the exception of response-based parameters, were found to be a function of coagulation rate.     

Sensory evaluation and in vitro protein digestibility of mung bean as affected by cooking time

Heat treatment is particularly important in the preparation of mung bean for consumption, from the point of view not only of acceptability but also of improvement on protein digestibility. Sensory evaluation of cooked mung bean in terms of taste, colour, aroma and texture has an organoleptic panel indicated that there was no significant difference (P < 0.05) between mung bean cooked for 25, 30, 35, 40 and 45 min. An in vitro protein digestibility assay was used to examine the effect of cooking time on protein digestibility. The applied non-linear mathematical model indicated a high correlation coefficient between experimental and predicted data (R² ≥ 0.999). A maximum improvement in in vitro protein digestibility (IVPD) of 4.83% was obtained by cooking for 38.6 min. No remarkable changes in most amino acids were found between raw and cooked samples except that tryptophan was decreased by 4.69% and a 10.29% loss of threonine occurred with the optimum cooking time. © 1999 Society of Chemical Industry