Application of Machine Learning to Assess the Quality of Food Products—Case Study: Coffee Bean

Przybył, Krzysztof; Gawrysiak-Witulska, Marzena; Bielska, Paulina; Rusinek, Robert; Gancarz, Marek; Dobrzański, Bohdan; Siger, Aleksander

doi:10.3390/app131910786

Cited by 8 publications

(5 citation statements)

References 56 publications

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“…AI will work to increase the efficiency of sourcing raw materials by, among other things, optimizing parameters in the process, which will help reduce food waste. The idea of repeatable processes to obtain homogeneous food products for quality uniformity is also increasingly possible with the help of product classification [ 13 ], contamination detection [ 14 ], and defects, as well as quality assessment of these foods [ 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Przybył,

Walkowiak,

Kowalczewski

2024

Foods

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In the modern times of technological development, it is important to select adequate methods to support various food and industrial problems, including innovative techniques with the help of artificial intelligence (AI). Effective analysis and the speed of algorithm implementation are key points in assessing the quality of food products. Non-invasive solutions are being sought to achieve high accuracy in the classification and evaluation of various food products. This paper presents various machine learning algorithm architectures to evaluate the efficiency of identifying blackcurrant powders (i.e., blackcurrant concentrate with a density of 67 °Brix and a color coefficient of 2.352 (E520/E420) in combination with the selected carrier) based on information encoded in microscopic images acquired via scanning electron microscopy (SEM). Recognition of blackcurrant powders was performed using texture feature extraction from images aided by the gray-level co-occurrence matrix (GLCM). It was evaluated for quality using individual single classifiers and a metaclassifier based on metrics such as accuracy, precision, recall, and F1-score. The research showed that the metaclassifier, as well as a single random forest (RF) classifier most effectively identified blackcurrant powders based on image texture features. This indicates that ensembles of classifiers in machine learning is an alternative approach to demonstrate better performance than the existing traditional solutions with single neural models. In the future, such solutions could be an important tool to support the assessment of the quality of food products in real time. Moreover, ensembles of classifiers can be used for faster analysis to determine the selection of an adequate machine learning algorithm for a given problem.

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Section: Introductionmentioning

confidence: 99%

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Przybył,

Walkowiak,

Kowalczewski

2024

Foods

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“…Kopi merupakan salah satu komoditas yang paling umum dikonsumsi di seluruh dunia, dan memiliki berbagai karakteristik yang membuatnya menjadi minuman yang unik [1]. Biji kopi digunakan dalam penyajian minuman kopi, yang merupakan minuman kedua paling banyak dikonsumsi di dunia [2].…”

Section: Pendahuluanunclassified

Color Histogram Dan Support Vectore Machine Untuk Mengklasifikasikan Biji Kopi Berdasarkan Tingkat Pemanggangan

Khotimatul Wildah,

Latif,

Agustiani

et al. 2024

jati

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Pemilihan biji kopi berdasarkan tingkat pemanggangan menjadi faktor kunci dalam menentukan rasa kopi yang dihasilkan. Untuk mengklasifikasikan biji kopi berdasarkan tingkat pemanggangan, biasanya penilaian dilakukan berdasarkan warna dan bentuk fisik biji kopi. Namun, variabilitas lingkungan dan kondisi individu dapat memengaruhi ketepatan penilaian tersebut. Oleh karena itu, diperlukannya deteksi otomatis mengenai klasifikasi biji kopi tersebut. Metode computer vision dengan teknik image classification dapat memberikan solusi terkait permasalahan tersebut. Penelitian ini memperkenalkan metode image processing menggunakan color histogram sebagai teknik ekstraksi fitur untuk merepresentasikan ciri-ciri visual biji kopi dalam berbagai tingkatan pemanggangan. Selanjutnya, Support Vector Machine (SVM) digunakan sebagai algoritma klasifikasi yang mampu mengelompokkan biji kopi berdasarkan distribusi warna yang dihasilkan dari histogram tersebut. Pendekatan ini bertujuan untuk meningkatkan ketepatan klasifikasi biji kopi berdasarkan tingkat pemanggangan, dengan mengurangi pengaruh faktor subjektif individu serta meningkatkan konsistensi dalam proses pengklasifikasian. Hasil eksperimen menunjukkan bahwa penggabungan color histogram dengan SVM mampu memberikan metode yang andal dan akurat dalam mengklasifikasikan biji kopi berdasarkan tingkat pemanggangan dengan nilai akurasi sebesar 98,95%.

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“…One study applied convolutional neural networks [9] in food reviews to classify the quality of products using images as inputs; this task was achieved by segmenting the contents of the plate into multiple sections. Another similar study [10] used Computer Vision to analyze the color of coffee beans and classify their quality. In addition, machine learning is capable of assessing the quality of products through large-scale reviews with the assistance of demographic data or food product data.…”

Section: Introductionmentioning

confidence: 99%

AliAmvra—Enhancing Customer Experience through the Application of Machine Learning Techniques for Survey Data Assessment and Analysis

Mpouziotas,

Besharat,

Tsoulos

et al. 2024

Information

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AliAmvra is a project developed to explore and promote high-quality catches of the Amvrakikos Gulf (GP) to Artas’ wider regions. In addition, this project aimed to implement an integrated plan of action to form a business identity with high added value and achieve integrated business services adapted to the special characteristics of the area. The action plan for this project was to actively search for new markets, create a collective identity for the products, promote their quality and added value, engage in gastronomes and tasting exhibitions, dissemination and publicity actions, as well as enhance the quality of the products and markets based on the customer needs. The primary focus of this study is to observe and analyze the data retrieved from various tasting exhibitions of the AliAmvra project, with a target goal of improving customer experience and product quality. An extensive analysis was conducted for this study by collecting data through surveys that took place in the gastronomes of the AliAmvra project. Our objective was to conduct two types of reviews, one focused in data analysis and the other on evaluating model-driven algorithms. Each review utilized a survey with an individual structure, with each one serving a different purpose. In addition, our model review focused its attention on developing a robust recommendation system with said data. The algorithms we evaluated were MLP (multi-layered perceptron), RBF (radial basis function), GenClass, NNC (neural network construction), and FC (feature construction), which were used for the implementation of the recommendation system. As our final verdict, we determined that FC (feature construction) performed best, presenting the lowest classification rate of 24.87%, whilst the algorithm that performed the worst on average was RBF (radial basis function). Our final objective was to showcase and expand the work put into the AliAmvra project through this analysis.

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Application of Machine Learning to Assess the Quality of Food Products—Case Study: Coffee Bean

Cited by 8 publications

References 56 publications

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Color Histogram Dan Support Vectore Machine Untuk Mengklasifikasikan Biji Kopi Berdasarkan Tingkat Pemanggangan

AliAmvra—Enhancing Customer Experience through the Application of Machine Learning Techniques for Survey Data Assessment and Analysis

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