Deep transfer learning to verify quality and safety of ground coffee

Pradana-López, Sandra; Pérez-Calabuig, Ana M.; Cancilla, John C.; Lozano, Miguel Ángel; Rodrigo, Carlos; Mena, M.L.; Torrecilla, José S.

doi:10.1016/j.foodcont.2020.107801

Cited by 35 publications

(10 citation statements)

References 24 publications

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“…The studies showed that Cadmium, Chromium, Lead, Mercury, Nickel (Siddharth et al, 2017) and Cobalt are classified in the group with moderate to high toxicity (Bauer, 2013). Silver ion is a highly toxic substance in terms of function (as an enzymatic and metabolic inhibitor) and biochemically silver ions (Ag+) can act as a potent inhibitor of some enzyme (Pradana-lópez et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

Evaluation of heavy metals in Roasted Coffee powder in Iran and Turkey

Massoud

MirMohammadMakki

Makki³

et al. 2022

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Nowadays coffee is a popular beverage around the world used in many food industries such as chocolate, dairy industry and also confectionery. Therefore, its quality required special attention. On the other hand, heavy metals have been attracted the attention in food products due to their toxicity and health risks in the food chain. Since coffee is a desirable widely used drink in the world for different age groups, this study aims to measure the concentration of heavy metals and some other elements in several brands of coffee powder from Iran and Turkey markets. After sample collection and preparation, heavy metals were measured using ICP-MS according to the AOAC method. The results showed that fortunately lead, cadmium, cobalt, silver, chromium, and mercury were not detected in any coffee samples. Also, it was observed that Nickel was only found in 3 samples, which was less than the standard limits. The pH of all samples was measured and the range was 5.03 to 6.32 in Iranian and Turkish samples. However, there was a lack of evidence in heavy metals amounts in coffee, this study reveals successful practical information in this field in Iranian and Turkish market but also according to the importance of this issue, further comprehensive studies is needed all over the world.

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Section: Resultsmentioning

confidence: 99%

Evaluation of heavy metals in Roasted Coffee powder in Iran and Turkey

Massoud

MirMohammadMakki

Makki³

et al. 2022

View full text Add to dashboard Cite

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“…Furthermore, an artificial neural network (ANN) was also popular to detect counterfeiting of Muscatel wines (Cancilla et al, 2020), edible bird's nests (90% accuracy) (Huang et al, 2019), and coffee chemical compounds (such as phenol, pH, and coffee purity) up to 90% accuracy and mean square error 0.0442 (Hendrawan et al, 2019). Above that, recent research in convolutional neural networks (CNN), another type of AI, gave more practical results on detecting honey adulteration resulting in up to 97.96% accuracy (Li et al, 2021;Izquierdo et al, 2020a), extra virgin olive oil with 97% accuracy (Izquierdo et al, 2020b), and counterfeiting in Arabica and Robusta coffee with an error below 1% (Lopez et al, 2021). Hence, CNN was more potential to detect coffee quality and authenticity than other learning machines.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

JST

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Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world.In order to lower production costs, Indonesian producers and retailers often mix highpriced Luwak coffee with regular coffee green beans. However, the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing method's development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories, very low (0-25%), low (25-50%), medium (50-75%), and high (75-100%). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then, the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet, GoogLeNet, and AlexNet. At the same time, the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm, Adam, RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001, which resulted in 89.65% accuracy. Furthermore, the testing process using confusion matrix from different sample data obtained the best

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“…SVM = Support Vector Machine, RBF = Radial Basis Function, CNN = Convolutional Neural Network, Pre = Pre-trained with ImageNet, HSI = Hyperspectral Imaging, PL = Polarized, LS = Laser Scatter, ch = channels, f = features, Mono = Monochrome, and IRAS = Infrared image Acquisition System. Medus et al 11 Thota et al 12 Prada-López et al 17 Xie et al 18 Barnes et al 40 Our method Quality control Sealing of heat-sealed food trays (Anomalies detected: washers, sugar, flange, plywood, cork, elastic rubber, wood, paper, sliver paper, hair, and polarized plastic) Presence and legibility of use-by date information from food packages Types of coffee and adulterations Atlantic salmon bone residues Detect copper wire in the seal of heat-sealed packages Sealing of thermoforming food packages Method (Accuracy) SVM RBF 84.3% CNN-ResNet18 96% CNN-ResNet18(Pre) 96% CNN-ResNet50 95% 94% CNN-ResNet50(Pre) 92.5% 97% CNN-ResNet34(Pre) 98.6% CNN-VGG16...…”

Section: Introductionmentioning

confidence: 99%

Deep learning for the quality control of thermoforming food packages

Banús

Boada

Xiberta

et al. 2021

Sci Rep

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Quality control is a key process designed to ensure that only products satisfying the defined quality requirements reach the end consumer or the next step in a production line. In the food industry, in the packaging step, there are many products that are still evaluated by human operators. To automate the process and improve efficiency and effectiveness, computer vision and artificial intelligence techniques can be applied. This automation is challenging since specific strategies designed according to the application scenario are required. Focusing on the quality control of the sealing and closure of matrix-shaped thermoforming food packages, the aim of the article is to propose a deep-learning-based solution designed to automatically perform the quality control while satisfying production cadence and ensuring 100% inline inspection of the products. Particularly, the designed computer vision system and the image-based criteria defined to determine when a product has to be accepted or rejected are presented. In addition, the vision control software is described with special emphasis on the different convolutional neural network (CNN) architectures that have been considered (ResNet18, ResNet50, Vgg19 and DenseNet161, non-pre-trained and pre-trained on ImageNet) and on the specifically designed dataset. To test the solution, different experiments are carried out in the laboratory and also in a real scenario, concluding that the proposed CNN-based approach improves the efficiency and security of the quality control process. Optimal results are obtained with the pre-trained DenseNet161, achieving false positive rates that range from 0.03 to 0.30% and false negative rates that range from 0 to 0.07%, with a rejection rate between 0.64 and 5.09% of production, and being able to detect at least 99.93% of the sealing defects that occur in any production. The modular design of our solution as well as the provided description allow it to adapt to similar scenarios and to new deep-learning models to prevent the arrival of faulty products to end consumers by removing them from the automated production line.

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Deep transfer learning to verify quality and safety of ground coffee

Cited by 35 publications

References 24 publications

Evaluation of heavy metals in Roasted Coffee powder in Iran and Turkey

Evaluation of heavy metals in Roasted Coffee powder in Iran and Turkey

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Deep learning for the quality control of thermoforming food packages

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