Adulteration Detection in Goat Dairy Beverage Through NIR Spectroscopy and DD-SIMCA

Teixeira, José Luan da Paixão; Caramês, Elem Tamirys dos Santos; Baptista, Débora Parra; Gigante, Mirna Lúcia

doi:10.1007/s12161-021-02151-9

Cited by 11 publications

(3 citation statements)

References 37 publications

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“…Therefore, this method offers potential for analysing cheese because the few recent studies that have used this method to classify dairy products did not classify cheese (Musa and Yang 2021; Teixeira et al . 2022).…”

Section: Resultsmentioning

confidence: 99%

“…Of the supervised methods used in this study, it was statistically proven that the DD-SIMCA method was the one that best classified the samples. Therefore, this method offers potential for analysing cheese because the few recent studies that have used this method to classify dairy products did not classify cheese (Musa and Yang 2021;Teixeira et al 2022).…”

Section: Chemometric Analysismentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of traditional semi‐hard Brazilian cheeses using chemometric tools to identify possible denomination fraud

Fitztum¹,

Lima

Judacewski³

et al. 2023

Int J of Dairy Tech

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This study aimed to evaluate and classify some of the main traditional Brazilian semi‐hard cheeses, using multivariate tools to determine possible denomination fraud. Fifty‐six samples of Minas Artisanal (MA), Colonial (AC), Minas Padrão (MP) and Meia Cura (MC) cheeses were analysed. All the evaluated parameters indicated high variability. PCA and HCA confirmed two groupings (AC and MP; and MA and MC) with high similarity. The DD‐SIMCA method classified the cheeses with 83% accuracy. AC (48%) and MA (20%) cheeses were the most well classified in their respective denomination. These results were efficient in classifying groups and demonstrating the lack of authenticity regarding the denominations of these cheeses.

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Section: Resultsmentioning

confidence: 99%

Section: Chemometric Analysismentioning

confidence: 99%

Evaluation of traditional semi‐hard Brazilian cheeses using chemometric tools to identify possible denomination fraud

Fitztum¹,

Lima

Judacewski³

et al. 2023

Int J of Dairy Tech

View full text Add to dashboard Cite

show abstract

“…With the rapid development of computer technology, people gradually apply industrial equipment and artificial intelligence to dairy adulteration identification (Neto et al, 2019). The mainstream methods are sensor technology (Teixeira, Caramês, Baptista, Gigante, & Pallone, 2022) , machine learning models (Asefa, Hagos, Kore, & Emire, 2022), and deep learning models (Z. Huang et al, 2023) .…”

Section: Related Workmentioning

confidence: 99%

EnDairy: An Ensemble Learning-based Framework for Efficient Dairy Product Identification

Wang

Yan

et al. 2023

Preprint

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Food safety has been a worldwide concern, and raw materials’ adulteration is oneof the significant factors contributing to this problem. Camel milk has high nutri-tional and economic value but faces adulteration problems. With time-consuming,poor stability, and low accuracy, the existing adulteration identification frame-works are limited applications. This paper proposes an efficient dairy adulterationidentification framework (EnDairy) based on an ensemble learning algorithmto solve these problems. Specifically, samples are identified by comparing theprobabilistic prediction values obtained and the optimal threshold by data pre-processing and weak learners iteration fusion into a strong learner. In this paper,we used a batch of camel milk data to verify the effectiveness of EnDairy in realscenarios. We tested its discriminative ability for data with missing values byfeature mask. The results show that EnDairy performs well; the AUC and Recallvalues are equal and 0.986 and 0.981 for the 10% and 20% feature mask treat-ments, respectively. The time taken by the identification model is only 0.637sand 0.648s. EnDairy ensures the accuracy of identification and effectively reducesthe economic and time costs, which can provide supportive information for camelmilk regulation, promote dairy product safety, and advance health for all.

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Machine learning and electrochemistry techniques for detecting adulteration of goat milk with cow milk

Xue,

Zhao

2024

Food Measure

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Adulteration Detection in Goat Dairy Beverage Through NIR Spectroscopy and DD-SIMCA

Cited by 11 publications

References 37 publications

Evaluation of traditional semi‐hard Brazilian cheeses using chemometric tools to identify possible denomination fraud

Evaluation of traditional semi‐hard Brazilian cheeses using chemometric tools to identify possible denomination fraud

EnDairy: An Ensemble Learning-based Framework for Efficient Dairy Product Identification

Machine learning and electrochemistry techniques for detecting adulteration of goat milk with cow milk

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