多光谱成像的玛咖掺伪定性鉴别和定量分析

引用本文

张宏蕊, 刘长虹, 张九凯, 韩建勋, 陈颖, 郑磊. 多光谱成像的玛咖掺伪定性鉴别和定量分析[J]. 光谱学与光谱分析, 2020,40(1): 152-156.
ZHANG Hong-rui, LIU Chang-hong, ZHANG Jiu-kai, HAN Jian-xun, CHEN Ying, ZHENG Lei. Qualitative Identification and Quantitative Analysis of Maca Adulteration Based on Multispectral Imaging Technology[J]. Spectroscopy and Spectral Analysis, 2020,40(1): 152-156.
Doi:10.3964/j.issn.1000-0593(2020)01-0152-05 复制到剪切板

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多光谱成像的玛咖掺伪定性鉴别和定量分析

张宏蕊^1,², 刘长虹¹, 张九凯², 韩建勋², 陈颖², 郑磊^1,^*

1. 合肥工业大学食品与生物工程学院, 安徽合肥 230009

2. 中国检验检疫科学研究院, 北京 100176

*通讯联系人 e-mail: lei.zheng@aliyun.com

作者简介: 张宏蕊, 1993年生, 合肥工业大学食品与生物工程学院硕士研究生 e-mail: zhanghongrui_18@163.com

收稿日期: 2018-11-13

基金: 国家重点研发计划项目(2017YFF0211004)资助

摘要

玛咖( Lepidium meyenii Walp.)是生长在高海拔地区的十字花科独行菜属一年生或两年生的草本植物, 具有丰富的营养价值和生物保健功效。自2011年被列为新资源食品后, 玛咖产业得到了迅速发展, 价格不断上涨。由于芜菁( Brassica rapa L.)外形与玛咖极为相似, 受经济利益驱使, 不法商家常将芜菁冒充玛咖, 制成玛咖粉、玛咖片和玛咖饮品等牟取暴利, 这给玛咖健康产业的有序发展带来了严重的负面影响。因此玛咖的真伪鉴别是非常必要的, 但目前对于玛咖的真伪鉴别多为传统方法, 快速检测方法较少。采用多光谱成像技术, 建立了一种玛咖与芜菁真伪的快速无损鉴别的新方法。主要包括玛咖切片和玛咖粉真伪鉴别两部分内容; 其一是针对玛咖切片的真伪鉴别, 通过Videometer A/S多光谱成像仪对240片玛咖和芜菁切片(玛咖和芜菁切片各120片)进行数据采集, 波长范围在可见光区域和近红外区域, 分别为405, 435, 450, 470, 505, 525, 570, 590, 630, 645, 660, 700, 780, 850, 870, 890, 910, 940和970 nm共19个波段, 为了有效鉴别玛咖和芜菁, 首先进行了主成分分析(principal component analysis, PCA), 然后结合支持向量机(support vector machines, SVM), 遗传算法优化支持向量机(genetic algorithm and support vector machine, GA-SVM)和反向传播神经网络(back propagation neural network, BPNN)算法建立了分析模型, 校正集与预测集的样品数量比值为3:1。研究发现PCA分析可以明显地将玛咖和芜菁区分, SVM模型对于玛咖和芜菁切片的预测正确率分别为98.33%, 100%, GA-SVM和BPNN模型对玛咖和芜菁切片的鉴别正确率均为100%。其二是针对玛咖粉的真伪鉴别, 选择120份玛咖粉, 向其中掺入20%, 40%, 60%, 80%, 4个不同掺假水平( W/W)的芜菁粉进行多光谱数据采集, 结合偏最小二乘(partial leastsquares, PLS)和最小二乘支持向量机(least squares-support vector machines, LS-SVM)对芜菁的掺假比例进行了定量预测。研究发现, PLS和LS-SVM模型对于玛咖粉中芜菁粉的掺入比例的预测决定系数(RP2)分别为0.992和0.994, 预测均方根误差(RMSEP)分别为2.718%和2.675%, 相对预测误差(RPD)分别为12.782和12.987。相比较而言, LS-SVM模型具有更高的Rp2, RPD较低的RMSEP, 对于玛咖粉中掺入芜菁粉比例的预测性能更好。为玛咖真伪的快速无损鉴别提供了一种新方法。

关键词: 玛咖; 芜菁; 真伪鉴别; 多光谱成像; 无损检测

中图分类号:O657.3 文献标志码:A

Qualitative Identification and Quantitative Analysis of Maca Adulteration Based on Multispectral Imaging Technology

ZHANG Hong-rui^1,², LIU Chang-hong¹, ZHANG Jiu-kai², HAN Jian-xun², CHEN Ying², ZHENG Lei^1,^*

1. School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China

2. Chinese Academy of Inspection and Quarantine, Beijing 100176, China

*Corresponding author

Abstract

Maca ( Lepidium meyenii Walp.), an annual or biennial herb of Brassicaceae family, grows at high altitudes and contains rich nutritional value and bio-health benefits. After being listed as a new resource food in 2011, Maca is gradually becoming familiar to the public, the Maca industry has developed rapidly and price has risen steadily. Due to the fact that the shape of turnip ( Brassica rapa L.) is very similar to that of Maca, driven by economic interests, illegal businessmen often pass turnip off as Maca to make Maca powder, slices and drinks in order to make exorbitant profits, which has brought serious negative impact on the orderly development of Maca healthy industry. Therefore, the authenticity identification of Maca is very necessary, but most of methods for the authenticity identification of Maca are traditional, and there are few rapid detection methods. In this study, a new method for rapid and non-destructive identification of Maca and turnip was established by using multispectral imaging technology. The experiment mainly focuses on the authenticity identification of Maca slices and Maca powder. One is to identify the authenticity of Maca slices. A total of 240 Maca and turnip slices (120 Maca slices and 120 turnip slices, respectively) were selected to collect data by the Videometer Lab equipment, which acquired the multispectral images at 19 different wavelengths from the visual region to the lower wavelengths of the NIR region and the detailed information of the measured wavelength were 405, 435, 450, 470, 505, 525, 570, 590, 630, 645, 660, 700, 780, 850, 870, 890, 910, 940 and 970 nm. In order to identify Maca and turnip effectively, the principal component analysis (PCA) was first performed. Then the qualitative analysis model was generated using support vector machine (SVM), genetic algorithm optimization support vector machine (GA-SVM) and back propagation neural network (BPNN) algorithm, and the ratio of the the calibration set to the prediction set is 3:1. The results demonstrated that clear differences between Maca and turnip could be easily visualized by PCA. The predictive accuracies by SVM model for Maca and turnip slices were 98.33% and 100%, respectively, and the predictive accuracies by GA-SVM and BPNN model could be as high as 100%. The other is the identification of Maca powder. 120 samples of Maca powder were selected and 20%, 40%, 60%, 80%, 4 different adulterated levels (W/W) of turnip powder were mixed for multispectral data acquisition, Combining partial least squares (PLS) and least squares support vector machine (LS-SVM), the adulteration ratio of turnip was quantitatively predicted. The study found that the prediction coefficient (RP2) of PLS and LS-SVM models were 0.992 and 0.994, the predicted root mean square error (RMSEP) were 2.718% and 2.675% and the relative prediction error (RPD) were 12.782 and 12.987, respectively. In comparison, the LS-SVM model had higherRP2, RPD, lower RMSEP, so it was considered to have better predictive performance for the proportion of turnip powder adulterated to Maca powder. In conclusion, the research results provide a method for the rapid and non-destructive identification of Maca authenticity.

Keyword: Maca; Turnip; Authentication; Multispectral imaging; Non-destructive detection

文章图片

引言

玛咖原产于南美秘鲁海拔3 500 m以上的安第斯山脉^[1], 在秘鲁当地食用和药用历史已有2000多年, 具有“ 南美人参” 的美誉。它含有多种次生代谢产物如玛咖酰胺、玛咖烯、芥子油苷和甾醇等^[2], 具有提高生育力、抗疲劳、神经保护和缓解更年期综合征^[3]等功效。 2002年在我国云南、西藏等地相继引种成功^[4], 2011年以来产业高速发展, 但随着玛咖价格的上涨, 利润空间加大化, 掺假现象愈发严重, 产品质量参差不齐。

目前玛咖真伪鉴别方法主要包括液相色谱、质谱和DNA条形码等。例如, Zhang等基于液相色谱和液相色谱质谱联用分析了不同产地玛咖的芥子油苷, 为玛咖的质量评估提供了依据^[5]; 李绍辉等利用超高效液相色谱串联质谱法检测玛咖中的非法添加物^[6]; Chen等通过DNA条形码对玛咖及其掺假物进行了鉴别^[7]。然而色谱、质谱技术样品预处理复杂, 设备昂贵, 需要消耗大量的化学试剂, DNA条形码需要专门的数据库作为支撑。近几年红外光谱和电子鼻技术等快速检测技术开始用于玛咖的分类鉴别, 如王元忠等分别利用红外光谱和近红外光谱对玛咖的产地进行了鉴别分类^{[8, 9]}; 党艳婷等基于玛咖中芥子油苷含量通过电子鼻技术对玛咖进行了品质等级分类^[10], 实现了玛咖品质的快速鉴定。但对于玛咖的掺假研究尚少。作为一种快速无损检测技术, 多光谱成像在质量监控方面有着很大的潜力, 应用于农产品无损检测领域, 主要有三个方向: 一是农产品品质的预测分级, 如Martina等利用多光谱成像成功地将被镰刀菌感染的小麦种子与未感染的小麦种子区分, 为小麦种子的质量评估和筛选提供了依据^[11]; 二是加工过程中的质量监测, 如脱水胡萝卜片的颜色和水分含量是其质量监测的重要指标, Liu等用多光谱实现了实时监测胡萝卜切片在热空气脱水情况下颜色和水分的变化情况^[12]; 三是真伪掺假的在线鉴别, 如Liu等通过多光谱成像有效地鉴别出注水牛肉, 而且检测出了牛肉样本中增加的水分含量^[13]; Xiong等采用多光谱成像结合多种化学计量学方法成功辨别了辐照虾和非辐照虾, 为辐照水产品的检测提供了方法^[14]。本研究利用多光谱成像结合SVM, GA-SVM和BPNN模型对玛咖及其掺假物芜菁切片进行了分类鉴别; 利用PLS和LS-SVM模型对玛咖粉中掺入芜菁粉的比例进行了定量预测, 旨在为玛咖的掺伪定性定量分析提供一种方法。

1 实验部分

1.1 材料与设备

玛咖干果(采购于云南宁蒗县, 西藏, 经云南省农科院科学院药用植物研究所鉴定), 芜菁为市售。

Videometer A/S多光谱成像仪(丹麦); QY-2B型多功能中药切片机(温州顶历医疗器械有限公司); HC-100T型多功能粉碎机(河城工贸有限公司); bencbtopk冷冻干燥机(美国VirTis公司)。

1.2 方法

1.2.1 样品制备

玛咖和芜菁干果利用自动切片机切成3 mm厚度的薄片, 冷冻干燥24 h。玛咖和芜菁切片分别来自不同的玛咖和芜菁干果, 各120片, 其中任意选择180片为校正集, 60片为预测集。进行多光谱数据采集。切片实验完成后, 模拟市场上玛咖粉掺假, 将切片粉碎成细粉末, 过60目筛。然后芜菁粉以20%, 40%, 60%, 80%, 共4个掺假水平(W/W)掺到玛咖粉中。另外, 留有纯的玛咖粉和芜菁粉用于实验。将两种粉末充分搅拌混合均匀, 将混匀后的样品置于培养皿中, 使培养皿表面平整, 然后利用多光谱测量仪对不同掺假水平的样品进行光谱测定, 每份样品由不同的玛咖和芜菁干果磨成的粉组成, 校正集90份样品, 预测集30份样品。

1.2.2 多光谱图像采集

多光谱成像仪可以测量405, 435, 450, 470, 505, 525, 570, 590, 630, 645, 660, 700, 780, 850, 870, 890, 910, 940和970 nm共19个波段的光谱反射值, 覆盖了可见光以及近红外波长。多光谱成像采集系统由摄像头、 LED 灯、积分球等部分组成, 测量时将样品放于积分球内, 积分球表面涂有白色颜料, 光线可以均匀传播, 光谱敏感摄像头置于积分球的顶部, LED灯置于积分球边缘位置。多光谱图像是含有光谱数据和形态信息值的三维图像, 原始图像里包含了噪声和冗杂信息, 需要进行预处理后再提取光谱值。

1.2.3 数据分析

采用的化学计量学方法包括PCA, SVM, GA-SVM, LS-SVM, BPPN, PLS, 其中需要的软件有Origin 8.5和Matlab 2011a。

PCA是一种常用的无监督的多元统计学方法, 通过运用少量的特征对样品进行描述, 达到降低特征空间维数的目的。 PLS是一种线性回归的多元校准模型, BPPN和线性模型相比, 更能准确地解决复杂的问题, 在模式识别中有广泛的应用。 SVM 是一个可以进行非线性分类、函数估计和核密度估计的一种新型算法, 具有较好的泛化能力, 广泛应用于统计分析和回归分析中。 LS-SVM是在SVM算法基础上作了改进, 可以同时进行线性和非线性建模分析, 可以快速准确地进行多元建模^[15]。 GA-SVM是将支持向量机和遗传算法结合起来的一种混合算法, 遗传算法提取最优的特征子集, 支持向量机确定特征子集的合适值。

1.2.4 模型校准

模型构建后需要评价样本测量值和预测值间的回归模型, 判断模型是否准确可靠和稳定, 主要通过校准均方根误差(RMSEC)、预测均方根误差(RMSEP)、偏差(bias)、校准(R_C)和预测(R_P)中的决定系数(R²)以及相对预测误差(RPD)来进行评估。 RPD值越大则预测化学成分准确率越高, 一般RPD大于3可以用于质量控制。通常来说一个好的模型应该具有较高的 $R_{C}^{2}$ , $R_{P}^{2}$ 和RPD以及较低的RMSEC, RMSEP和bias^[16]。

2 结果与讨论

2.1 玛咖及其掺假物芜菁的光谱分析

玛咖及其掺假物芜菁在405~970 nm波长范围内的平均反射光谱变化趋势相似, 两者随着波长的增加, 反射值逐渐增加。但是存在一些差异, 玛咖的反射值始终高于芜菁的反射值[图1(a)], 这反映了玛咖及芜菁中物理和化学成分存在差异。在405~700 nm可见光范围内, 玛咖反射值高于芜菁的反射值主要是和样品的颜色有关。在940~970 nm范围内玛咖和芜菁光谱值差异主要是由于O— H伸缩或弯曲模式引起的。在不同比例芜菁粉和玛咖粉平均反射光谱中[图1(b)], 所有样品的平均光谱呈相似的变化趋势, 随着波长的增加呈上升趋势。样品的反射值与掺入芜菁粉比例呈正相关增长, 其中掺入60%芜菁粉、掺入80%芜菁粉和芜菁粉3组之间反射值递增的梯度较为明显, 另外在700~850 nm范围内6组反射值差异最为显著。由于玛咖中成分复杂, 仅依靠化学成分来定性分析是困难的, 因此应用化学计量学方法进一步分析光谱数据以获取更好的玛咖鉴别结果。

	Figure Option View Download New Window
	图1 玛咖和芜菁的平均反射光谱 (a): 玛咖和芜菁切片; (b): 玛咖粉及掺入不同比例的芜菁粉Fig.1 Average reflectance spectra of Maca and turnip (a): Slices of Maca and turnip; (b): Maca powder with different adulterated levels of turnip powder

2.2 玛咖及其掺假物芜菁切片的分类分析

为了对玛咖和芜菁进行有效鉴别, 首先采用PCA分析以观察主成分空间中玛咖及其掺假物芜菁样品之间的变化和区分能力。玛咖及其掺假物芜菁的主成分三维得分图结果显示, 三个主成分PC1, PC2, PC3总得分达99.63%, 得分依次为93.46%, 4.23%, 1.94%。可清晰地将玛咖及其掺假物芜菁分成两组(图2)。结果表明玛咖和芜菁可以被区分并且样品的不同光谱属性和样品的特征是相关的。在PCA可视化分析基础之上建立SVM, GA-SVM, BPNN等模型进一步准确验证。 240个玛咖和芜菁样品(玛咖、芜菁各120), 分为2组, 其中校正集180个, 预测集60个。 3个模型鉴别结果如表1所示, 在SVM模型的预测集中, 只有1个玛咖样品没有正确分类, 玛咖和芜菁的预测正确率分别为98.33%, 100%。在GA-SVM和BPNN模型中, 玛咖和芜菁样品全部正确分类, 预测正确率均为100%。 3个模型相比, GA-SVM和BPNN模型更适于玛咖和芜菁的鉴别, 表明多光谱成像结合GA-SVM或BPNN模型可以有效实现玛咖及其掺假物芜菁的鉴别。

	Figure Option View Download New Window
	图2 玛咖和芜菁光谱的三维主成分分析图Fig.2 3-D principal component analysis of the Maca and turnip spectra

表1 用于鉴别玛咖及芜菁切片的SVM, GA-SVM, BPNN模型结果对比 Table 1 Comparison of SVM, GA-SVM, and BPNN model results for identifying Maca and turnip slices

2.3 掺假玛咖粉中芜菁粉的定量预测

在光谱分析中, 最佳校正模型的选择是非常重要的。利用PLS, LS-SVM算法建立校准模型对向玛咖粉中掺入20%, 40%, 60%, 80%的芜菁粉进行了预测, 共120份样品, 其中校正集90份, 预测集30份。两个模型预测的结果如表2所示。根据最低PRESS值确定了PLS模型的潜变量个数为5, 通过留一交叉验证法得到LS-SVM模型预测芜菁粉比例的最佳参数(γ , δ ²)为(1 024, 0.008 9)。通过评估 $R_{C}^{2}$ , $R_{P}^{2}$ , RMSEC, RMSEP, RPD和bias选出最佳的分析模型, 最佳模型应具有较高的 $R_{C}^{2}$ , $R_{P}^{2}$ 和RPD以及较低的RMSEC, RMSEP和bias。表2中LS-SVM模型的决定系数 $R_{C}^{2}$ , $R_{P}^{2}$ , 分别为0.997, 0.994, 略高于PLS模型( $R_{C}^{2}$ , $R_{P}^{2}$ 分别为0.996, 0.992), 预测集的RMSEP和bias分别为2.675%, 0.032%, 低于PLS模型的RMSEP 2.718%和bias 1.635%。另外, RPD作为验证模型准确性的重要参数, 在LS-SVM模型中为12.987高于PLS模型。通常RPD值大于8, 被认为模型实际应用的预测能力非常好。因此LS-SVM模型预测玛咖粉中掺入芜菁粉比例的能力较高, 相比于PLS模型更适于玛咖粉中芜菁粉掺入比例的预测。 PLS模型预测能力稍弱的原因可能是因为它是线性回归的方法, 而LS-SVM是非线性的算法, 有更强的自学和调整能力, 而且玛咖成分复杂, 非线性方法更适合。

表2 用于预测玛咖粉中芜菁粉掺假比例的PLS和LS-SVM模型结果对比 Table 2 Comparison of PLS and LS-SVM models for predicting the percentage of adulteration of turnip powder in Maca powder

方法	校正集				预测集
方法	$R_{C}^{2}$	RMSEC/%	RPD	bias/%	$R_{P}^{2}$	RMSEP/%	RPD	bias/%)
PLS	0.996	2.132	16.079	-9.144× 10^-13	0.992	2.718	12.782	1.635
LS-SVM	0.997	1.536	22.366	0.099	0.994	2.675	12.987	0.032

注: $R_{C}^{2}$ : 校准绝对系数(coefficient of determination in calibration); $R_{P}^{2}$ : 预测绝对系数(coefficient of determination in prediction); RMSEC: 校准均方根误差(root mean square error of calibration); RMSEP: 预测均方根误差(root mean square error of prediction); RPD: 相对预测误差(residual predictive deviation); bias: 偏差

目前市场上向玛咖粉中掺入芜菁粉的量多少不一, 而且分布范围较广, 实时鉴别难度较大。本研究的主要目的是应用多光谱成像实现玛咖粉中掺假芜菁粉检测的可能性, 通过对20%, 40%, 60%, 80%的掺假比例研究后发现, 掺假玛咖样本的多光谱信号随掺假比例呈现规律性变化, 证明了该方法进行芜菁掺假定量分析是可行的。在将来的研究中, 需要对模型预测的最低检测水平进行研究, 并对定量分析的稳定性进行优化, 使得建立的模型能够更好地应用于市场上玛咖掺假的快速无损检测。总之, 多光谱成像结合多元统计学方法在玛咖真伪鉴别方面有很大的发展空间和应用前景。

3 结论

本研究的创新性主要在于利用多光谱成像结合不同化学计量学方法, 包括PCA, SVM, GA-SVM, BPNN, PLS和LS-SVM对玛咖及芜菁切片进行了鉴别以及对玛咖粉中芜菁粉的掺入比例进行了预测。结果表明, 对玛咖及芜菁切片的鉴别, 两者可以成功区分, GA-SVM和BPNN模型有较好的校准和预测能力, 准确率为100%; 对玛咖粉中掺入芜菁粉比例的预测, LS-SVM模型的预测性能最佳。多光谱成像结合化学计量学方法可以为玛咖掺伪定性定量分析提供一种快速无损有效的新方法。

参考文献

文献列表

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2014

0.0

... 引言玛咖原产于南美秘鲁海拔3 500 m以上的安第斯山脉^[1], 在秘鲁当地食用和药用历史已有2000多年, 具有#cod#x0201c ...

2017

0.0

... 它含有多种次生代谢产物如玛咖酰胺、玛咖烯、芥子油苷和甾醇等^[2], 具有提高生育力、抗疲劳、神经保护和缓解更年期综合征^[3]等功效 ...

2015

0.0

... 它含有多种次生代谢产物如玛咖酰胺、玛咖烯、芥子油苷和甾醇等^[2], 具有提高生育力、抗疲劳、神经保护和缓解更年期综合征^[3]等功效 ...

2016

0.0

YANG

Min

, HUANG

Shao-jun

, ZHU

Yan-qin

, et al(杨敏, 黄绍军, 朱艳琴, 等). Food Chemistry(食品科学), 2016, 37(2): 132.

The volatile components of maca from 8 producing regions in Yunnan province were extracted by headspace solid-phase micro-extraction (HS-SPME) and identified by gas chromatography-mass spectrometry (GC-MS). A total of 69 volatile constituents were identified, of which 18 were common to all the tested samples. The identified volatile constituents included 12 ketones, 10 aldehydes, 9 alcohols, 7 acids, 6 esters, 6 alkenes, 5 heterocyclic compounds, 4 nitriles, 4 aromatic compounds, 3 phenols, 2 amines and 1 sulfoether compound. Among these volatile constituents, benzyl isothiocyanates, benzaldehyde, benzyl cyanide, acetic acid, benzyl alcohol, dimethyl sulfide, 3-trimethoxy-benzaldehyde, 3-methoxyphenyl acetonitrile, 4-methoxybenzyl isothiocyanates and N-phenylmethylene-benzenemethanamine were predominant. Comparison by Excel and SPSS suggested that there were some differences in the type and content of volatile components identified from maca samples from 8 producing areas in Yunnan province.

Research Center for Analysis and Measurement, Kunming University of Science and Technology, Kunming 650093, China

采用顶空固相微萃取技术对玛咖中挥发性成分进行富集，用气相色谱-质谱联用技术对其化学成分进行分离和鉴定，在云南8 个种植区玛咖样品中共检出挥发性成分69 种，共有的挥发性成分有18 种。这些成分包括酮类12 种、醛类10 种、醇类9 种、酸类7 种、酯类6 种、烯烃类6 种、杂环化合物5 种、腈类4 种、芳香族类4 种、酚类3 种、胺类2 种、硫醚类1 种。含量较高的挥发性成分是异硫氰酸苄酯、苯甲醛、苯乙腈、乙酸、苯甲醇、二甲基硫醚、3-甲氧基-苯甲醛、4-甲氧基苄基异硫氰酸酯。通过统计比较发现了云南8 个不同种植区玛咖挥发性成分存在的差异。

... 2002年在我国云南、西藏等地相继引种成功^[4], 2011年以来产业高速发展, 但随着玛咖价格的上涨, 利润空间加大化, 掺假现象愈发严重, 产品质量参差不齐 ...

2017

0.0

... 例如, Zhang等基于液相色谱和液相色谱质谱联用分析了不同产地玛咖的芥子油苷, 为玛咖的质量评估提供了依据^[5] ...

2016

0.0

... 李绍辉等利用超高效液相色谱串联质谱法检测玛咖中的非法添加物^[6] ...

2015

0.0

... Chen等通过DNA条形码对玛咖及其掺假物进行了鉴别^[7] ...

2016

0.0

WANG

Yuan-zhong

, ZHAO

Yan-li

, ZHANG

, et al(王元忠, 赵艳丽, 张霁, 等). Food Chemistry(食品科学), 2016, 37(4): 169.

Based on Fourier transform infrared spectroscopy (FTIR), identification of the origin of 139 samples of maca collected from Yunnan and Peru was conducted. The infrared spectra were preprocessed by multiple scattering correction combined with second derivative and Norris smoothing. Through eliminating the noise spectral bands, the suitable number of principal components was chose as eight. Based on the optimal number of principal components, by using interval partial least squares (iPLS), the spectra in the range of 3 650.59–651.82 cm–1 was processed by optimization analysis. An iPLS-DA classification model was built by screening the spectra of 98 samples in the ranges of 1 855.19–651.822, 3 054.69–2 756.78 and 3 650.59–3 353.6 cm–1. The R2, RMSEC and RMSEP of the model were 0.958 4, 0.785 8 and 1.164 2, respectively. The verification with 41 samples indicated that the validation accuracy was consistent with that of the classification model built using the original spectra, which was 87.80%. To further improve the accuracy of the classification model on the basis of iPLS screening of spectral bands, the spectral information was optimized by genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA), respectively. The results showed that, through GA screening the frequency of spectral information which was greater than 4 and 5, the filtered spectral data points were 62 and 29, respectively. Through SFLA screening the probability of spectral information which was greater than 0.1 and 0.15, the filtered spectral data points were 77 and 27, respectively. The validation results showed that the recognition efficiency of the classification model built by GA-PLS-DA (62 data points) and GA-PLS-DA (29 data points) were 95.12% and 97.56%, respectively. The recognition efficiency of the classification model built by SFLA-PLS-DA (77 data points) and SFLA-PLS-DA (27 data points) were 92.68% and 97.56%. By comparing the above methods, we could find that the classification models built by iPLS-DA, GA-PLS-DA and SFLAPLS- DA all had good prediction performance, of which the models built by GA-PLS-DA (29 data points) and SFLA-PLSDA (27 data points) could more accurately identify the different origins of maca. The methods could provide a new way for identification of the origin of maca with IR. The screening of the spectral variables could provide the basis for the difference analysis of the chemical constitutes (components) in different origins of maca.

Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China

采用傅里叶变换红外光谱法，对采自云南及秘鲁共139 份玛咖样品进行产地鉴别研究。采用多元散射校正结合二阶导数和Norris平滑预处理光谱，通过剔除噪声明显的光谱波段，筛选出适宜的主成分数为8。基于最优主成分数，采用间隔偏最小二乘（interval partial least-squares，iPLS）法对3 650.59～651.82 cm－1光谱进行优化分析。结果显示，筛选98 份样品在1 855.19～651.822、3 054.69～2 756.78 cm－1和3 650.59～3 353.6 cm－1光谱建立的间隔偏最小二乘判别分析（interval partial least-squares discriminant analysis，iPLS-DA）分类模型，其R2、校正均方根误差和预测均方根误差分别为0.958 4、0.785 8和1.164 2。通过41 份样品验证，验证正确率与原光谱建立的分类模型保持一致，均为87.80%。为进一步提高分类模型的精度，在iPLS筛选的光谱波段基础上，分别采用遗传算法（geneticalgorithm，GA）和蛙跳算法（shuffled frog leaping algorithm，SFLA）对光谱信息进行优化，结果显示，采用GA筛选频率大于4和5的光谱信息，筛选的光谱数据点分别为62 个和29 个；利用SFLA筛选概率大于0.1和0.15的光谱信息，筛选的光谱数据点分别为77 个和27 个。验证结果显示，采用GA-PLS-DA（62 个数据点）和GA-PLS-DA（29 个数据点）建立的PLS-DA分类模型识别正确率分别为95.12%和97.56%，采用SFLA-PLS-DA（77 个数据点）和SFLA-PLS-DA（27 个数据点）建立的分类模型识别正确率分别为92.68%和97.56%。对比上述方法可知，采用iPLS-DA、GA-PLS-DA和SFLA-PLSDA建立的分类模型均具有较好的预测性能，其中GA-PLS-DA（29 个数据点）和SFLA-PLS-DA（27 个数据点）建立分类模型能更准确地鉴别不同产地的玛咖。该方法的建立为玛咖红外光谱产地鉴别提供一种新的思路，所筛选的光谱变量可为不同产地玛咖内在化学成分（组分）差异性分析提供基础依据。

... 近几年红外光谱和电子鼻技术等快速检测技术开始用于玛咖的分类鉴别, 如王元忠等分别利用红外光谱和近红外光谱对玛咖的产地进行了鉴别分类^[8,9] ...

2016

0.0

2018

0.0

... 党艳婷等基于玛咖中芥子油苷含量通过电子鼻技术对玛咖进行了品质等级分类^[10], 实现了玛咖品质的快速鉴定 ...

2016

0.0

... 作为一种快速无损检测技术, 多光谱成像在质量监控方面有着很大的潜力, 应用于农产品无损检测领域, 主要有三个方向: 一是农产品品质的预测分级, 如Martina等利用多光谱成像成功地将被镰刀菌感染的小麦种子与未感染的小麦种子区分, 为小麦种子的质量评估和筛选提供了依据^[11] ...

2016

0.0

... 二是加工过程中的质量监测, 如脱水胡萝卜片的颜色和水分含量是其质量监测的重要指标, Liu等用多光谱实现了实时监测胡萝卜切片在热空气脱水情况下颜色和水分的变化情况^[12] ...

2016

0.0

... 三是真伪掺假的在线鉴别, 如Liu等通过多光谱成像有效地鉴别出注水牛肉, 而且检测出了牛肉样本中增加的水分含量^[13] ...

2016

0.0

... Xiong等采用多光谱成像结合多种化学计量学方法成功辨别了辐照虾和非辐照虾, 为辐照水产品的检测提供了方法^[14] ...

2015

0.0

... LS-SVM是在SVM算法基础上作了改进, 可以同时进行线性和非线性建模分析, 可以快速准确地进行多元建模^[15] ...

2017

0.0

... 通常来说一个好的模型应该具有较高的 RC2, RP2和RPD以及较低的RMSEC, RMSEP和bias^[16] ...