Artificial Intelligence Aided Adulteration Detection and Quantification for Red Chilli Powder

Sarkar, Tanmay; Choudhury, Tanupriya; Bansal, Nikunj; Arunachalaeshwaran, V. R.; Khayrullin, Mars; Shariati, Mohammad Ali; Lorenzo, José M.

doi:10.1007/s12161-023-02445-0

Cited by 7 publications

(3 citation statements)

References 66 publications

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“…However, the inherent seasonal and regional characteristics of agricultural production, compounded by market uncertainties and environmental concerns, such as water quality and health risks associated with agricultural inputs (Mohammadpour et al, 2024a(Mohammadpour et al, , 2024b, render traditional financial strategies inadequately equipped to address the distinct requisites of agricultural trade (Bai et al, 2023;Yusuf et al, 2022;Zhao et al, 2023). The advent of machine learning technologies heralds a new era, offering innovative perspectives and tools for the meticulous prediction and optimization of agricultural financial strategies, thereby enabling a more scientific and efficacious approach to strategy formulation (Sarkar et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Enhancing agricultural product trade efficiency through machine learning predictions and multi-objective optimization of financial strategies

2024

Qual. Assur. Saf. Crops Foods

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In the context of global economic development and food safety, agricultural trade plays a vital role in linking agricultural production with market demand, and the efficient formulation of agricultural financial strategies is crucial for enhancing trade efficiency. This study employs advanced machine learning technologies to predict and optimize the efficiency of agricultural trade while seeking to balance the interests of various stakeholders in agricultural finance. By integrating quantile factor models, long short-term memory (LSTM) networks, and attention mechanisms, this paper conducts an in-depth analysis and precise prediction of the key factors affecting trade efficiency. This approach effectively addresses the nonlinearity and long-term dependency issues in time-series data, utilizing attention mechanisms to highlight critical information and improve prediction accuracy. Furthermore, this research establishes a multi-objective optimization model to balance the interests of agricultural finance participants, providing a new quantitative tool for formulating agricultural financial strategies aimed at optimizing decisions to enhance economic and social value. This paper offers new perspectives, methods, and empirical support for improving the efficiency of agricultural trade and the formulation of agricultural financial strategies.

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Section: Introductionmentioning

confidence: 99%

Enhancing agricultural product trade efficiency through machine learning predictions and multi-objective optimization of financial strategies

2024

Qual. Assur. Saf. Crops Foods

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“…Compared with bulky foods, powdered foods are much easier for unscrupulous sellers to adulterate in order to reduce the raw material costs. Currently reported adulterants in chili powder mainly include Sudan I-IV dyes [4][5][6][7], brick powder [7,8], red beetroot [9,10], almond shell [8,11], dried tomato peel [11], and starch [11]. These adulterants pose significant health risks to consumers.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Khan et al [12] investigated the adulterants in chili powder by using a spectroscopic method. Sarkar et al [7] used machine-learning algorithms to classify the highquality images of chili-brick powder adulterants. These methods showed good results in the detection of adulterants.…”

Section: Introductionmentioning

confidence: 99%

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Peng,

Ba,

Zhang

et al. 2024

Applied Sciences

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Driven by economic interests, adding adulterations in chili powder is a problem which threatens people’s health. Thanks to its nondestructive, rapid, and portable advantages, electronic nose has more potential to be used for adulteration detection than the traditional methods. An approach for identifying the adulterants in chili powder was proposed in this paper. Firstly, an electronic nose system with 10 gas sensors was designed, and then the response images were drawn based on the response signals of the electronic nose. Afterwards, gas features were extracted from those response images by using a histogram of oriented gradients (HOG) algorithm. Finally, an SVM-based identification model was constructed to achieve the identification of plant adulterants in chili powder. The experimental results showed that the identification accuracy of the adulterant categories (almond shell, red beetroot, and tomato peel) based on the HOG features could reach up to 98.3%, and the identification results for adulterant content were 94.2%, 93.3%, and 95%, respectively. Furthermore, in order to compare the efficiency of the proposed identification approach, the widely used model AlexNet was also investigated and discussed.

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Chicken Quality Evaluation Using Deep Learning

Madan

Choudhury

Sarkar

et al. 2023

Lecture Notes in Networks and Systems

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Artificial Intelligence Aided Adulteration Detection and Quantification for Red Chilli Powder

Cited by 7 publications

References 66 publications

Enhancing agricultural product trade efficiency through machine learning predictions and multi-objective optimization of financial strategies

Enhancing agricultural product trade efficiency through machine learning predictions and multi-objective optimization of financial strategies

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Chicken Quality Evaluation Using Deep Learning

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