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

doi:10.3969/j.issn.1004-1524.20260090

Acta Agriculturae Zhejiangensis ›› 2026, Vol. 38 ›› Issue (6): 1112-1125.DOI: 10.3969/j.issn.1004-1524.20260090

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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 Online:2026-06-25 Published:2026-07-14

Abstract

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

CLC Number:

TS252.56

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.

Figures/Tables 7

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.

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

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

Fig.3 Principal component analysis score plot

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

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.

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.

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

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