基于近红外光谱技术的西藏牦牛酥油掺假快速无损鉴别

doi:10.3969/j.issn.1004-1524.20260090

浙江农业学报 ›› 2026, Vol. 38 ›› Issue (6): 1112-1125.DOI: 10.3969/j.issn.1004-1524.20260090

基于近红外光谱技术的西藏牦牛酥油掺假快速无损鉴别

李文珍¹^,²^,³(), 梅涵一²^,³, 王树林¹, 聂晶²^,³, 李春霖²^,³, 张永志²^,³, 邓丰涛⁴, 袁玉伟²^,³^,^*()

¹ 青海大学农牧学院, 青海西宁 810016
² 浙江省农业科学院农产品质量安全与营养研究所, 浙江杭州 310021
³ 农业农村部农产品信息溯源重点实验室, 浙江杭州 310021
⁴ 浙江红谱科技有限公司, 浙江杭州 310030

收稿日期:2026-02-04 出版日期:2026-06-25 发布日期:2026-07-14
作者简介:李文珍,研究方向为食品加工与安全。E-mail: 3483295317@qq.com
通讯作者: ^*袁玉伟,E-mail: yuanyw@zaas.ac.cn
基金资助:
西藏自治区科技计划项目(课题)(XZ202402ZY0007);浙江省农业科学院学科建设专项经费项目(ZAAS2025)

Rapid and non-destructive identification of adulteration in Xizang yak ghee based on near infrared spectroscopy

LI Wenzhen¹^,²^,³(), MEI Hanyi²^,³, WANG Shulin¹, NIE Jing²^,³, LI Chunlin²^,³, ZHANG Yongzhi²^,³, DENG Fengtao⁴, YUAN Yuwei²^,³^,^*()

¹ College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China
² Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
³ Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310021, China
⁴ Zhejiang Hongpu Technology Co., Ltd., Hangzhou 310030, China

Received:2026-02-04 Published:2026-06-25 Online:2026-07-14

摘要/Abstract

摘要：

本研究旨在建立一种基于近红外光谱(near infrared spectroscopy,NIR)技术的西藏牦牛酥油掺假快速、无损鉴别方法。以采集自西藏林芝和那曲的传统手工牦牛酥油为研究对象,按不同比例(0、20%、40%和60%)掺入棕榈油、牛油和人造奶油制备掺假样品,并测定其酸价、含水量、过氧化值、脂肪含量等理化指标;采用主成分分析(principal component analysis,PCA)对NIR光谱特征进行无监督降维与结构分析,并在PCA基础上构建有监督支持向量机(support vector machine,SVM)和k近邻(k-nearest neighbor, KNN)模型进行掺假鉴别。结果表明,不同掺假油脂对牦牛酥油理化指标的影响呈特异性规律:酸价偏低或含水量降低可能存在棕榈油掺假;酸价偏高或过氧化值上升可能存在高比例牛油掺假;含水量显著偏高是人造奶油掺假的关键依据;脂肪含量可与其他3个指标联合作为酥油掺假鉴别依据。PCA得分图显示,不同类别样品在主成分空间中存在一定分布差异,但类别间仍有重叠,难以形成清晰判别边界。SVM和KNN模型在独立测试集上的分类准确率均达到100%。综上,NIR原始光谱吸光度变化与掺假类型及其比例高度相关,结合化学计量学方法能够实现西藏牦牛酥油的有效掺假识别,为传统牦牛酥油品质保障提供了一种高效可靠的技术手段。

关键词: 近红外光谱技术, 西藏牦牛酥油, 掺假鉴别, 品质

Abstract:

This study aimed to establish a rapid and non-destructive adulteration identification method for Xizang yak ghee by using near infrared spectroscopy (NIR) technology. Traditional handmade yak ghee was collected from Nyingchi and Nagqu in Xizang as research materials. Adulterated samples were prepared by mixing palm oil, beef tallow, and margarine into yak ghee at different proportions (0, 20%, 40% and 60%). The physicochemical indices including acid value, moisture content, peroxide value and fat content were determined. Principal component analysis (PCA) was applied to perform unsupervised dimensionality reduction and structural analysis on NIR spectral characteristics. On the basis of PCA results, supervised chemometric models including support vector machine (SVM) and k-nearest neighbor (KNN) were constructed to realize ghee adulteration identification. The results showed that different adulterated oils presented specific effects on the physicochemical characteristics of yak ghee. Lower acid value and decreased moisture content could be regarded as characteristic indicators of palm oil adulteration; higher acid value and increased peroxide value were closely related to high-proportion beef tallow adulteration; significantly increased moisture content was the key discrimination index for margarine adulteration. Fat content could be combined with the other three physicochemical indices to jointly identify adulteration in yak ghee. PCA score plots indicated that samples with different adulteration types and proportions showed partial distribution differences in the principal component space, while obvious category overlaps still existed, resulting in unclear discriminative boundaries. Both the SVM and KNN models achieved 100% classification accuracy on the independent test set.In conclusion, the absorbance changes of original NIR spectra were highly correlated with adulteration types and adulteration ratios of yak ghee. Combined with chemometric methods, NIR spectroscopy could efficiently and accurately identify adulteration in Xizang traditional yak ghee. This study provided an efficient, reliable and rapid technical reference for quality supervision and quality assurance of plateau characteristic yak ghee products.

Key words: near-infrared spectroscopy, Xizang yak ghee, adulteration identification, quality

中图分类号:

TS252.56

李文珍, 梅涵一, 王树林, 聂晶, 李春霖, 张永志, 邓丰涛, 袁玉伟. 基于近红外光谱技术的西藏牦牛酥油掺假快速无损鉴别[J]. 浙江农业学报, 2026, 38(6): 1112-1125.

LI Wenzhen, MEI Hanyi, WANG Shulin, NIE Jing, LI Chunlin, ZHANG Yongzhi, DENG Fengtao, YUAN Yuwei. Rapid and non-destructive identification of adulteration in Xizang yak ghee based on near infrared spectroscopy[J]. Acta Agriculturae Zhejiangensis, 2026, 38(6): 1112-1125.

图/表 7

图1 掺假酥油的理化特征那曲酥油在相同掺假物下不同掺假水平间无相同大写字母表示差异显著(p<0.05),林芝酥油在相同掺假物下不同掺假水平间无相同小写字母表示差异显著(p<0.05);数据为5次重复的平均值±标准误。

Fig.1 Physical and chemical characteristics of adulterated yak ghee Nagqu yak ghee with the same adulterant marked without the same uppercase letter between different adulteration levels indicates significant (p<0.05) differences, and Nyingchi yak ghee with the same adulterant marked without the same lowercase letter between different adulteration levels indicates significant (p<0.05) differences. Data are presented as mean±standard error of five replicates.

图2 西藏掺假牦牛酥油样品原始光谱(a)和平均光谱(b)

Fig.2 Original spectra (a) and average spectra (b) of adulterated Xizang yak ghee

表1 前5个主成分的方差贡献率与累计方差贡献率

Table 1 Variance contribution rates and cumulative variance contribution rate of the top 5 principal components

主成分 Principal component	方差贡献率 Variance contribution rate	累计方差贡献率 Cumulative variance contribution rate
1	89.4	89.4
2	8.3	97.7
3	1.9	99.6
4	0.3	99.9
5	0.1	99.9

图3 主成分分析得分图

Fig.3 Principal component analysis score plot

表2 基于原始光谱和PCA降维后光谱的掺假西藏牦牛酥油判别准确率

Table 2 Discriminant accuracy of adulterated Xizang yak ghee based on the original spectra and PCA dimensionality-reduced spectra

模型 Model	关键参数 Key parameter	数据类型 Data type	输入变量 Input variable	累计方差贡献率/% Cumulative variance contribution rate/%	训练集 Training set			预测集 Prediction set
模型 Model	关键参数 Key parameter	数据类型 Data type	输入变量 Input variable	累计方差贡献率/% Cumulative variance contribution rate/%	平均F1分数 Average F1 score	AUC	Kappa值 Kappa value	平均F1分数 Average F1 score	AUC	Kappa值 Kappa value
SVM	C=87.3	原始光谱Raw spectrum	800	—	1	1	1	1	1	1
KNN	k=5	原始光谱Raw spectrum	800	—	1	1	1	1	1	1
PCA-SVM	C=156.8	主成分得分 Principal component scores	3	≥99	1	1	1	1	1	1
PCA-KNN	k=5	主成分得分 Principal component scores	3	≥99	1	1	1	1	1	1

图4 西藏牦牛酥油掺假鉴别模型 a,SVM预测集;b,SVM训练集;c,KNN预测集;d,KNN训练集。绿色方块代表模型识别正确样本;橙色方块代表模型识别错误样本;矩阵最右侧一列为各行真实类别样本的分类召回率;矩阵最底部一行为各列预测类别样本的分类精确率;右下角灰色单元格为模型整体分类准确率。所有样本均实现100%正确分类。

Fig.4 Adulteration identification model of Xizang yak ghee a, SVM prediction set; b, SVM training set; c, KNN prediction set; d, KNN training set. Green squares represent correctly classified samples by the model. Orange squares represent misclassified samples. The rightmost column of the matrix indicates the recall rate for each row’s true class samples. The bottom row shows the precision rate for each column’s predicted class samples, and the gray square in the bottom-right corner displays the model’s overall classification accuracy. All samples achieved 100% correct classification.

图5 PCA降维后的西藏牦牛酥油掺假鉴别模型 a,PCA-SVM预测集;b,PCA-SVM训练集;c,PCA-KNN预测集;d,PCA-KNN训练集。绿色方块代表模型识别正确样本;橙色方块代表模型识别错误样本;矩阵最右侧一列为各行真实类别样本的分类召回率;矩阵最底部一行为各列预测类别样本的分类精确率;右下角灰色单元格为模型整体分类准确率。所有样本均实现100%正确分类。

Fig.5 Adulteration identification model of Xizang yak ghee after PCA dimensionality reduction a, PCA-SVM prediction set; b, PCA-SVM training set; c, PCA-KNN prediction set; d, PCA-KNN training set. Green squares represent correctly classified samples by the model; orange squares represent misclassified samples. The rightmost column of the matrix indicates the recall rate for each row’s true class samples, the bottom row shows the precision rate for each column’s predicted class samples, and the gray square in the bottom-right corner displays the model’s overall classification accuracy. All samples achieved 100% correct classification.

参考文献 35

[1]	杨婷婷, 孙万成, 罗毅皓, 等. 菜籽油、牦牛酥油和亚麻籽油调和油的制备及其稳定性研究[J]. 甘肃农业大学学报, 2026, 61(1): 274-279.
	YANG T T, SUN W C, LUO Y H, et al. Development and stability evaluation of a blended oil from rapeseed oil, yak chee and linseed oil[J]. Journal of Gansu Agricultural University, 2026, 61(1): 274-279.
[2]	谭大明, 吴雪莲, 邱城, 等. 西藏自治区酥油、糌粑、青稞酒、风干牛肉质量安全调研[J]. 现代农业科技, 2011(2): 368-369.
	TAN D M, WU X L, QIU C, et al. Investigation on the quality safety of Tibetan butter, tsampa, Qingke liquor and wind-dried yak meat in Tibet Autonomous Region of China[J]. Modern Agricultural Sciences and Technology, 2011(2): 368-369.
[3]	洪蕾, 杨丽杰, 张洋, 等. 精酥油的制作与脂肪酸组成分析[J]. 食品工业, 2013, 34(3): 190-192.
	HONG L, YANG L J, ZHANG Y, et al. The processing technology and fatty acid composition analysis of pure butter[J]. The Food Industry, 2013, 34(3): 190-192.
[4]	王浩文, 张峰轶, 王传明, 等. 不同市售牛油理化性质与风味品质的比较[J]. 中国油脂, 2025, 50(2): 46-50.
	WANG H W, ZHANG F Y, WANG C M, et al. Comparison of physicochemical properties and flavor quality of different commercially available beef tallow[J]. China Oils and Fats, 2025, 50(2): 46-50.
[5]	张阜青. 棕榈油基人造奶油品质缺陷及改善[D]. 无锡: 江南大学, 2009.
	ZHANG F Q. The study on quality defects and improvement of palm oil-based margarine[D]. Wuxi: Jiangnan University, 2009.
[6]	孙美青, 马莺, 程金菊. 牦牛酥油理化性质研究[J]. 中国乳品工业, 2016, 44(3): 9-11.
	SUN M Q, MA Y, CHENG J J. Physical and chemical properties of yak butter[J]. China Dairy Industry, 2016, 44(3): 9-11.
[7]	于海坤. 牛油与植物油复配脂肪酸平衡调和油的研究及品质分析[D]. 沈阳: 沈阳农业大学, 2022.
	YU H K. Study and quality analysis of fatty acid balanced blended oil from beef tallow and vegetable oil[D]. Shenyang: Shenyang Agricultural University, 2022.
[8]	李响, 王晓霞, 刘辰, 等. 近红外光谱在乳制品检测中应用技术研究进展[J]. 中国检验检测, 2025, 33(1): 24-27.
	LI X, WANG X X, LIU C, et al. Research progress on the application technology of near-infrared spectroscopy in dairy product detection[J]. China Inspection Body & Laboratory, 2025, 33(1): 24-27.
[9]	方芳, 王耀耀, 刘欣, 等. 基于近红外光谱技术的茶籽油掺伪定性鉴别和定量分析[J]. 中国粮油学报, 2023, 38(12): 203-210.
	FANG F, WANG Y Y, LIU X, et al. Qualitative identification and quantitative analysis of adulterated in Camellia seed oil based on near infrared spectroscopy technology[J]. Journal of the Chinese Cereals and Oils Association, 2023, 38(12): 203-210.
[10]	黄宝莹, 佘之蕴, 王文敏, 等. 近红外光谱技术在乳制品快速检测中的应用研究进展[J]. 中国酿造, 2020, 39(7): 16-19.
	HUANG B Y, SHE Z Y, WANG W M, et al. Research progress of near-infrared spectroscopy application in rapid detection of dairy products[J]. China Brewing, 2020, 39(7): 16-19.
[11]	洛曲, 于修烛, 张建新, 等. 基于近红外光谱的藏区酥油脂肪和蛋白质含量快速检测分析[J]. 中国油脂, 2018, 43(3): 136-140.
	LUO Q, YU X Z, ZHANG J X, et al. Rapid determination of fat and protein contents in ghee using near-infrared spectroscopy[J]. China Oils and Fats, 2018, 43(3): 136-140.
[12]	赵小伟, 郑楠, 王加启, 等. 近红外光谱技术在乳及乳制品质量检测中的应用[J]. 动物营养学报, 2024, 36(9): 5451-5459.
	ZHAO X W, ZHENG N, WANG J Q, et al. Application of near-infrared spectroscopy technology in milk and dairy products[J]. Chinese Journal of Animal Nutrition, 2024, 36(9): 5451-5459.
[13]	万恒兴, 余展旺, 冯丽雄. 二维相关近红外光谱的牦牛奶粉掺伪判别分析[J]. 山东化工, 2024, 53(20): 149-152.
	WAN H X, YU Z W, FENG L X. PLS-discriminant analysis of adulterated yak milk powder based on two-dimensional correlation near-infrared spectroscopy[J]. Shandong Chemical Industry, 2024, 53(20): 149-152.
[14]	那琴. 基于近红外光谱技术结合化学计量学的驼乳及乳粉掺假检测研究[D]. 呼和浩特: 内蒙古农业大学, 2022.
	NA Q. Research on adulteration detection of camel milk and milk powder based on near-infrared spectroscopy combined with chemometrics[D]. Hohhot: Inner Mongolia Agricultural University, 2022.
[15]	彭海洋, 巫忠东, 林涛, 等. 基于近红外光谱技术的牦牛奶粉掺假检测与产地识别研究[J]. 食品工业科技, 2024, 45(22): 9-17.
	PENG H Y, WU Z D, LIN T, et al. Adulteration detection and origin identification of yak milk powder based on near-infrared spectroscopy technology[J]. Science and Technology of Food Industry, 2024, 45(22): 9-17.
[16]	YU D X, GUO S, ZHANG X, et al. Combining stable isotope, multielement and untargeted metabolomics with chemometrics to discriminate the geographical origins of ginger (Zingiber officinale Roscoe)[J]. Food Chemistry, 2023, 426: 136577.
[17]	ROUDAK M A, FARAHANI M, HOSSEINBEIGI F B. Extension of K-nearest neighbors and introduction of an applicable prediction criterion for a novel Monte Carlo simulation-based method in structural reliability[J]. Structures, 2024, 66: 106867.
[18]	LIU D, SUN D W, ZENG X A. Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry[J]. Food and Bioprocess Technology, 2014, 7(2): 307-323.
[19]	周显青, 康招阳, 韩佳静, 等. 基于PCA-RF预测方便米饭品质模型的建立[J]. 河南工业大学学报(自然科学版), 2025, 46(2): 87-95.
	ZHOU X Q, KANG Z Y, HAN J J, et al. Establishment of a PCA-RF-based prediction model for the quality of instant rice[J]. Journal of Henan University of Technology(Natural Science Edition), 2025, 46(2): 87-95.
[20]	王国胜. 核函数的性质及其构造方法[J]. 计算机科学, 2006, 33(6): 172-174.
	WANG G S. Properties and construction methods of kernel in support vector machine[J]. Computer Science, 2006, 33(6): 172-174.
[21]	孙淑敏, 谢岩黎, 张严. 基于可见—近红外光谱的花生油二元掺伪体系鉴别研究[J]. 粮油食品科技, 2015, 23(6): 84-88.
	SUN S M, XIE Y L, ZHANG Y. Identification of adulterate peanut oil binary system based on visible-near infrared spectroscopy[J]. Science and Technology of Cereals, Oils and Foods, 2015, 23(6): 84-88.
[22]	罗香, 刘波平, 冯利辉, 等. 近红外光谱技术用于花生油中棕榈油含量的测定[J]. 分析科学学报, 2010, 26(6): 673-676.
	LUO X, LIU B P, FENG L H, et al. Determination of palm oil content in peanut oil samples by near infrared spectroscopy[J]. Journal of Analytical Science, 2010, 26(6): 673-676.
[23]	李梅, 智丽慧. 近红外光谱技术快速检测奶酪及其制品的核心质量指标[J]. 食品安全质量检测学报, 2025, 16(15): 51-56.
	LI M, ZHI L H. Rapid detection of core quality indicators in cheese and cheese products by near-infrared spectroscopy[J]. Journal of Food Safety and Quality, 2025, 16(15): 51-56.
[24]	庄小丽, 相玉红, 强洪, 等. 近红外光谱和化学计量学方法用于橄榄油品质分析与掺杂量检测[J]. 光谱学与光谱分析, 2010, 30(4): 933-936.
	ZHUANG X L, XIANG Y H, QIANG H, et al. Quality analysis of olive oil and quantification detection of adulteration in olive oil by near-infrared spectrometry and chemometrics[J]. Spectroscopy and Spectral Analysis, 2010, 30(4): 933-936.
[25]	BAI L, ZHANG Z X, LI Y L, et al. Combining stable isotopes and multi-elements with machine learning chemometric models to identify the geographical origins of Tetrastigma hemsleyanum Diels et Gilg[J]. Food Chemistry, 2025, 469: 142496.
[26]	刘红丽, 刘文捷, 毕艳兰, 等. 棕榈油在常温储存过程中品质变化的研究[J]. 河南工业大学学报(自然科学版), 2011, 32(2): 35-39.
	LIU H L, LIU W J, BI Y L, et al. Study on the quality change of palm oil during normal storage[J]. Journal of Henan University of Technology(Natural Science Edition), 2011, 32(2): 35-39.
[27]	李东锐, 毕艳兰, 肖新生, 等. 食用油煎炸过程中的品质变化研究[J]. 中国油脂, 2006, 31(6): 34-36.
	LI D R, BI Y L, XIAO X S, et al. Research on the change of edible oil quality during frying process[J]. China Oils and Fats, 2006, 31(6): 34-36.
[28]	刘佳敏, 何新益, 刘晓东, 等. 精炼对牛油主要理化指标及挥发性成分的影响[J]. 食品与机械, 2020, 36(4): 62-67.
	LIU J M, HE X Y, LIU X D, et al. Effect of refining on main physical and chemical indexes and volatile components of beef tallow[J]. Food and Machinery, 2020, 36(4): 62-67.
[29]	左青, 费楚琼, 胡建新, 等. 特种油脂在我国食品工业中的应用[J]. 中国油脂, 2025, 50(9): 153-155.
	ZUO Q, FEI C Q, HU J X, et al. Application of special oils in China’s food industry[J]. China Oils and Fats, 2025, 50(9): 153-155.
[30]	谢司伟, 刘春爱, 孙术国, 等. 西藏不同海拔牦牛酥油的营养、风味及功能特性比较[J]. 食品与机械, 2021, 37(2): 19-23.
	XIE S W, LIU C A, SUN S G, et al. Comparison of nutritional, flavor and functional characteristics of Tibetan yak butter at different altitudes[J]. Food & Machinery, 2021, 37(2): 19-23.
[31]	秦士博, 徐学兵, 张虹, 等. 人造奶油氧化稳定性的研究进展[J]. 中国油脂, 2020, 45(5): 58-62.
	QIN S B, XU X B, ZHANG H, et al. Advance in oxidative stability of margarine[J]. China Oils and Fats, 2020, 45(5): 58-62.
[32]	张琦. 酥油贮藏稳定性研究[D]. 天津: 天津商业大学, 2014.
	ZHANG Q. Research on storage stability of ghee[D]. Tianjin: Tianjin University of Commerce, 2014.
[33]	高浩, 高媛惠, 薛颖. 近红外光谱法在食品营养成分检测中的应用研究[J]. 食品安全导刊, 2025(3): 156-158.
	GAO H, GAO Y H, XUE Y. Research on the application of near-infrared spectroscopy in the detection of food nutrients[J]. China Food Safety Magazine, 2025(3): 156-158.
[34]	周禹, 张睿加, 袁伟东, 等. 近红外光谱及成像检测影响因素与校正方法研究进展[J]. 激光与光电子学进展, 2024, 61(4): 29-38.
	ZHOU Y, ZHANG R J, YUAN W D, et al. Research progress on influencing factors and correction methods of near infrared spectroscopy and imaging[J]. Laser & Optoelectronics Progress, 2024, 61(4): 29-38.
[35]	邓焯文. 基于近红外光谱和计算机视觉技术结合深度学习算法的山茶油真实性鉴别研究[D]. 海口: 海南大学, 2024.
	DENG Z W. Authenticity identification of camellia oil based on near-infrared spectroscopy and computer vision techniques combined with deep learning[D]. Haikou: Hainan University, 2024.

基于近红外光谱技术的西藏牦牛酥油掺假快速无损鉴别

Rapid and non-destructive identification of adulteration in Xizang yak ghee based on near infrared spectroscopy

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