Rapid and accurate classification of Aspergillus ochraceous contamination in Robusta green coffee bean through near-infrared spectral analysis using machine learning

Ruttanadech, Nuttapong; Phetpan, Kittisak; Srisang, Naruebodee; Srisang, Siriwan; Chungcharoen, Thatchapol; Limmun‬, Warunee; Youryon, Pannipa; Kongtragoul, Pornprapa

doi:10.1016/j.foodcont.2022.109446

Cited by 16 publications

(3 citation statements)

References 31 publications

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“…LDA is regarded as a robust classifier, with an accuracy above 90% in studies on classification using HSI [ 13 ]. The superior performance of LDA in comparison with other machine learning methods was observed in classification studies of coffee bean contamination [ 31 ] and the infection of brown rice [ 32 ]. This is likely because LDA maximizes the rate between class variance and the interior of the class variance in any particular dataset to ensure the greatest separability [ 33 ].…”

Section: Resultsmentioning

confidence: 99%

Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging

Park,

Cho,

Lee

et al. 2023

Foods

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This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), or 0:100 (P0), and Allura Red was added to the red pepper pericarp/seed mixture at 0.05% (A), 0.1% (B), and 0.15% (C). The results of principal component analysis (PCA) using the L, a, and b values; hue angle; and chroma showed that the pure pericarp powder (P100) was not easily distinguished from some adulterated samples (P50A-C, P75A-C, and P100B,C). Adulterated red pepper powder was detected by applying machine learning techniques, including linear discriminant analysis (LDA), linear support vector machine (LSVM), and k-nearest neighbor (KNN), based on spectra obtained from SWIR (1,000–1,700 nm). Linear discriminant analysis determined adulteration with 100% accuracy when the samples were divided into four categories (acceptable, adulterated by Allura Red, adulterated by seeds, and adulterated by seeds and Allura Red). The application of SWIR technology and machine learning detects adulteration with Allura Red and seeds in red pepper powder.

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Section: Resultsmentioning

confidence: 99%

Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging

Park,

Cho,

Lee

et al. 2023

Foods

View full text Add to dashboard Cite

show abstract

“…They further often exhibit a restricted focus, tailored to particular regions [82,89] or coffee types [35,45,54,64,68,74,85,90,94,97] and further demonstrate a limited scope in identifying nuanced characteristics like roast degrees [47] and maturity stages [60,61]. The need for more universally applicable models is evident, especially in areas like aromatic profiling, where ML remains underutilized, revealing significant untapped potential for research and industry applications.…”

Section: Challenges and Future Trendsmentioning

confidence: 99%

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

de C. Motta,

Vuillerme,

Pham

et al. 2024

Preprint

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In the realm of agribusiness, transformative shifts are underway, propelled by the growing demands and expanding scales of grain production. This evolution calls for a critical reevaluation of the existing paradigms in coffee production and marketing paradigms, with a specific focus on integrating Artificial Intelligence (AI). This work aims to review, synthesize, and summarize the available data regarding how Machine Learning (ML) has been used to detect and classify characteristics in coffee beans and leaves. For this purpose, a comprehensive literature review of the most significant research contributions describing the application of AI for advanced classification techniques in coffee agriculture has been carried out. Our analysis suggests that implementing AI technologies allows the classification of coffee, encompassing various attributes such as maturity, roast intensity, disease identification, flavor profiles, and overall quality. More largely, this technological advancement holds the potential to revolutionize coffee farming by providing producers and agricultural specialists with sophisticated tools to enhance production efficiency, minimize costs, and improve the accuracy and confidence of their decision-making processes.

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“…Other recent studies that addressed coffee classification using infrared spectroscopy approaches include but are not limited to [34][35][36][37].…”

Section: Infrared Spectroscopymentioning

confidence: 99%

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

et al. 2023

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This study investigates the classification of Arabic coffee into three major variations (light, medium, and dark) using simulated data gathered from the actual measurements of color information, antioxidant laboratory testing, and chemical composition tests. The goal is to overcome the restrictions of limited real-world data availability and the high costs involved with laboratory testing. The Monte Carlo approach is used to generate new samples for each type of Arabic coffee using the mean values and standard deviations of publicly available data. Using these simulated data, multiple machine-learning algorithms are used to classify Arabic coffee, while also investigating the importance of features in identifying the key chemical components. The findings emphasize the importance of color information in accurately recognizing Arabic coffee types. However, depending purely on antioxidant information results in poor classification accuracy due to increased data complexity and classifier variability. The chemical composition information, on the other hand, has exceptional discriminatory power, allowing faultless classification on its own. Notably, particular characteristics like crude protein and crude fiber show high relationships and play an important role in coffee type classification. Based on these findings, it is suggested that a mobile application be developed that uses image recognition to examine coffee color while also providing chemical composition information. End users, especially consumers, would be able to make informed judgments regarding their coffee preferences.

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Rapid and accurate classification of Aspergillus ochraceous contamination in Robusta green coffee bean through near-infrared spectral analysis using machine learning

Cited by 16 publications

References 31 publications

Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging

Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

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