Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

Liang, Ning; Sun, Sashuang; Zhang, Chu; He, Yong; Qiu, Zhengjun

doi:10.1080/10408398.2020.1862045

Cited by 45 publications

(21 citation statements)

References 148 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, subjecting all the models to the voting technique improves the classification performance. It is also worth noting that deep learning and other machine learning approaches have recently been successful in various food-related spectroscopic data analysis ( Zhu et al, 2021 ; Zheng et al, 2014 ; Feng et al, 2021 ; Wang et al, 2021 ; Liang et al, 2020 ; Yan et al, 2021 ; He et al, 2021 ); thus, we expect that the considered machine learning and deep learning methods will work-well in the classification of various adulterant honey samples using NMR spectroscopy in the proposed context as well. As schematically shown in Fig.…”

Section: Resultsmentioning

confidence: 93%

Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification

Rachineni¹,

Kakita

Awasthi³

et al. 2022

Current Research in Food Science

View full text Add to dashboard Cite

Nuclear magnetic resonance (NMR) is a powerful analytical tool which can be used for authenticating honey, at chemical constituent levels by enabling identification and quantification of the spectral patterns. However, it is still challenging, as it may be a person-centric analysis or a time-consuming process to analyze many honey samples in a limited time. Hence, automating the NMR spectral analysis of honey with the supervised machine learning models accelerates the analysis process and especially food chemistry researcher or food industry with non-NMR experts would benefit immensely from such advancements. Here, we have successfully demonstrated this technology by considering three major sugar adulterants, i.e., brown rice syrup, corn syrup, and jaggery syrup, in honey at varying concentrations. The necessary supervised machine learning classification analysis is performed by using logistic regression, deep learning-based neural network, and light gradient boosting machines schemes.

show abstract

Section: Resultsmentioning

confidence: 93%

Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification

Rachineni¹,

Kakita

Awasthi³

et al. 2022

Current Research in Food Science

View full text Add to dashboard Cite

show abstract

“…The relevant studies in this category mainly use image data (Adem & Közkurt, 2019; Kodors et al., 2020; Patsekin et al., 2019; Song et al., 2019; Vo et al., 2020), sensor data (mainly from spectroscopy and electronic noses) (Liang, Sun et al., 2020; Liu et al., 2020; Mithun et al., 2018; Tsakanikas et al., 2020; Weng et al., 2020), and text data derived from online media, emails, and reports (Mao et al., 2018; Vo et al., 2020). The most frequently used sources of unstructured data related to food safety have been reviewed recently (Jin et al., 2020; Zhou et al., 2019).…”

Section: Resultsmentioning

confidence: 99%

Application of machine learning to the monitoring and prediction of food safety: A review

Wang

Bouzembrak

Lansink

et al. 2021

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, several studies have reviewed ML applications on foodborne disease and deep learning applications on food. This article presents a literature review on ML applications for monitoring and predicting food safety. The paper summarizes and categorizes ML applications in this domain, categorizes and discusses data types used for ML modeling, and provides suggestions for data sources and input variables for future ML applications. The review is based on three scientific literature databases: Scopus, CAB Abstracts, and IEEE. It includes studies that were published in English in the period from January 1, 2011 to April 1, 2021. Results show that most studies applied Bayesian networks, Neural networks, or Support vector machines. Of the various ML models reviewed, all relevant studies showed high prediction accuracy by the validation process. Based on the ML applications, this article identifies several avenues for future studies applying ML models for the monitoring and prediction of food safety, in addition to providing suggestions for data sources and input variables.

show abstract

“…The approximate wavelength absorption ranges of different components used in NIRS assessment are shown in Figure 2. The absorption wavelengths of the specific chemical structures can be used to interpret the performance of linear regression models (e.g., the PLSR model), but this will not work for nonlinear regression models (Chen et al., 2008; Liang et al., 2022; Du et al., ). The second category is physical parameters other than composition.…”

Section: Application Of Nirs In Wheat Quality Evaluationmentioning

confidence: 99%

“…NIRS was first studied in the early 1970s by Karl Norris and Phil Williams, who combined NIRS with chemometrics methods to measure protein and moisture contents (Manley, 2014; Norris & Williams, 1984; Williams et al., 2019). Subsequently, studies and reviews of NIRS applications significantly increased (Caporaso et al., 2018a; Lasztity & Abonyi, 2009; Liang et al., 2022; Mutlu et al., 2011; Pojić & Mastilović, 2013). While there are many studies combining NIRS with advanced chemometrics for wheat quality assessment, to our knowledge, there is no comprehensive and in‐depth review for readers who have expertise in physicochemical experiments but lack knowledge in spectral data analysis, or for companies to improve the applications of NIRS in wheat quality assessment.…”

Section: Introductionmentioning

confidence: 99%

Quantitative assessment of wheat quality using near‐infrared spectroscopy: A comprehensive review

Tian

Tilley

et al. 2022

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

Wheat is one of the most widely cultivated crops throughout the world. A great need exists for wheat quality assessment for breeding, processing, and products production purposes. Near‐infrared spectroscopy (NIRS) is a rapid, low‐cost, simple, and nondestructive assessment method. Many advanced studies associated with NIRS for wheat quality assessment have been published recently, either introducing new chemometrics or attempting new assessment parameters to improve model robustness and accuracy. This review provides a comprehensive overview of NIRS methodology including its principle, spectra pretreatments, spectral wavelength selection, outlier disposal, dataset division, regression methods, and model evaluation. More importantly, the applications of NIRS in the determination of analytical parameters, rheological parameters, and end product quality of wheat are summarized. Although NIRS showed great potential in the quantitative determination of analytical parameters, there are still challenges in model robustness and accuracy in determining rheological parameters and end product quality for wheat products. Future model development needs to incorporate larger databases, integrate different spectroscopic techniques, and introduce cutting‐edge chemometrics methods. In addition, calibration based on external factors should be considered to improve the predicted results of the model. The NIRS application in micronutrients needs to be extended. Last, the idea of combining standard product sensory attributes and spectra for model development deserves further study.

show abstract

Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

Cited by 45 publications

References 148 publications

Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification

Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification

Application of machine learning to the monitoring and prediction of food safety: A review

Quantitative assessment of wheat quality using near‐infrared spectroscopy: A comprehensive review

Contact Info

Product

Resources

About