Rapid detection of adulteration of glutinous rice as raw material of Shaoxing Huangjiu (Chinese Rice Wine) by near infrared spectroscopy combined with chemometrics

Peng, Qi; Meng, Kai; Zheng, Huajun; Chen, Guangqian; Xu, Xin; Lin, Zi-Han; Xie, Guangfa

doi:10.1016/j.jfca.2022.104563

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Following the present results, previous studies have shown that NIR spectroscopy and volatile organic compound (VOCs) detection techniques are rapid and non-invasive methods to detect fraud in foods [18,35,38,39] and drinks [33,40]. These include their application developed for fighting rice fraud, noting its popularity for the risk of adulteration along the supply chain [3,9,41].…”

Section: Resultssupporting

confidence: 68%

Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning

Aznan

Viejo

Pang

et al. 2022

Sensors

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Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice's weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94–0.98) and non-invasive measurement through the packaging (NIR; R = 0.95–0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain.

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Section: Resultssupporting

confidence: 68%

Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning

Aznan

Viejo

Pang

et al. 2022

Sensors

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“…It has been popular with Chinese customers for centuries ( 43 ), providing significant commercial value as a GI product. The fraudulent use of the Shaoxing brand for producing many yellow wines in non-Shaoxing areas necessitates the development of a rapid identification technique to verify authenticity ( 44 ). GC-IMS differentiated between 122 Chinese yellow wines from Shandong, Hubei, and Shaoxing ( 7 ).…”

Section: Traceability and Characteristic Identification Of Gi Product...mentioning

confidence: 99%

Application of GC-IMS coupled with chemometric analysis for the classification and authentication of geographical indication agricultural products and food

Zhu,

Sun

2023

Front. Nutr.

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Geographical indications (GI) are used to protect the brand value of agricultural products, foodstuffs, and wine and promote the sustainable development of the agricultural and food industries. Despite the necessity for the traceability and recognition of GI product characteristics, no rapid, non-destructive approaches currently exist to identify, classify, and predict these properties. The application of gas chromatography-ion mobility spectrometry (GC-IMS) has increased exponentially due to instrument robustness and simplicity. This paper provided a detailed overview of recent GC-IMS applications in China for the quality evaluation of GI products and food, including agricultural products, as well as traditional Chinese food and liquor. The general workflow of GC-IMS coupled with chemometric analysis is presented, including sample collection, model construction and interpretation, and data acquisition, processing, and fusion. Several conclusions are drawn to increase partial least squares-discriminant analysis (PLS-DA) model precision, a chemometric technique frequently combined with GC-IMS.

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“…Near-infrared spectroscopy (NIR) has the advantages of simple, rapid, and non-destructive, etc. It has been widely used in agriculture, food, medicine, and other fields [1][2] . NIRS technology has great application value in starch quality inspection.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of starch species based on near-infrared spectroscopy and quaternion convolution neural network

Tan

Zhao

Wang

et al. 2023

AOPC 2022: Optical Spectroscopy and Imaging

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In this paper, the near-infrared spectral data of five different types of starch were collected, and the starch species identification model was constructed by using a quaternion convolutional neural network (QCNN), we proved that the qualitative model based on QCNN has obtained higher prediction accuracy than traditional qualitative models. In the experimental results, the classification accuracy of QCNN for five different starches reached 0.996. The results show that the combination of the quaternion spectral fusion method and deep learning is more conducive to extracting and mining the deep information of NIR spectra and has important research significance and application value in the field of nearinfrared spectroscopy technology.

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Rapid detection of adulteration of glutinous rice as raw material of Shaoxing Huangjiu (Chinese Rice Wine) by near infrared spectroscopy combined with chemometrics

Cited by 5 publications

References 26 publications

Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning

Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning

Application of GC-IMS coupled with chemometric analysis for the classification and authentication of geographical indication agricultural products and food

Quantitative analysis of starch species based on near-infrared spectroscopy and quaternion convolution neural network

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