Quality assessment of fruit juices by NIR spectroscopy

Šnurkovič, Petr

doi:10.11118/actaun201361030803

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…By training the models with a broader range of spectral information, the variations attributed to sample preparation and Vis-NIRs analysis would be mitigated. It should be noted that there are studies based on spectroscopy data in which better results were obtained with PLS than with SVR [ 53 ], while in other cases, the opposite is reported [ 54 ] or even similar performances [ 55 ].…”

Section: Resultsmentioning

confidence: 99%

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning

Calle,

Punta-Sánchez,

González-de-Peredo

et al. 2023

Foods

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Honey is one of the most adulterated foods, usually through the addition of sweeteners or low-cost honeys. This study presents a method based on visible near infrared spectroscopy (Vis-NIRs), in combination with machine learning (ML) algorithms, for the correct identification and quantification of adulterants in honey. Honey samples from two botanical origins (orange blossom and sunflower) were evaluated and adulterated with low-cost honey in different percentages (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%). The results of the exploratory analysis showed a tendency to group the samples according to botanical origin, as well as the presence of adulteration. A supervised analysis was performed to detect the presence of adulterations. The best performance with 100% accuracy was achieved by support vector machines (SVM) and random forests (RF). A regression study was also carried out to quantify the percentage of adulteration. The best result was obtained by support vector regression (SVR) with a coefficient of determination (R2) of 0.991 and a root mean squared error (RMSE) of 1.894. These results demonstrate the potential of combining ML with spectroscopic data as a method for the automated quality control of honey.

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Section: Resultsmentioning

confidence: 99%

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning

Calle,

Punta-Sánchez,

González-de-Peredo

et al. 2023

Foods

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“…By using radial basis function neural network classifiers, the authors acquired a classification accuracy of 97.62%. Šnurkovič [ 62 ] demonstrated discrimination between a mixture of various juices that were adulterated with water, sugar, and fruit juices which was based on NIR spectral data. Several other investigators reported that infrared spectroscopy, specifically the MIR spectral region, has successfully been applied for authentication and adulteration testing [ 14 , 58 , 63 ].…”

Section: Infrared Spectroscopymentioning

confidence: 99%

Non-Invasive Methods for Predicting the Quality of Processed Horticultural Food Products, with Emphasis on Dried Powders, Juices and Oils: A Review

Okere

Arendse

Nieuwoudt

et al. 2021

Foods

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This review covers recent developments in the field of non-invasive techniques for the quality assessment of processed horticultural products over the past decade. The concept of quality and various quality characteristics related to evaluating processed horticultural products are detailed. A brief overview of non-invasive methods, including spectroscopic techniques, nuclear magnetic resonance, and hyperspectral imaging techniques, is presented. This review highlights their application to predict quality attributes of different processed horticultural products (e.g., powders, juices, and oils). A concise summary of their potential commercial application for quality assessment, control, and monitoring of processed agricultural products is provided. Finally, we discuss their limitations and highlight other emerging non-invasive techniques applicable for monitoring and evaluating the quality attributes of processed horticultural products. Our findings suggest that infrared spectroscopy (both near and mid) has been the preferred choice for the non-invasive assessment of processed horticultural products, such as juices, oils, and powders, and can be adapted for on-line quality control. Raman spectroscopy has shown potential in the analysis of powdered products. However, imaging techniques, such as hyperspectral imaging and X-ray computed tomography, require improvement on data acquisition, processing times, and reduction in the cost and size of the devices so that they can be adopted for on-line measurements at processing facilities. Overall, this review suggests that non-invasive techniques have the potential for industrial application and can be used for quality assessment.

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“…This technique has previously been used to detect the addition of sugars in apple juice [ 21 ] and bayberry juice [ 26 ]. Additionally, it has been successfully used to determine the quality of juice from different fruits [ 27 ], as well as to determine the addition of water in bayberry juice [ 28 ] and for the detection of adulterations in lime juice [ 29 ]. However, to the best of the authors’ knowledge, this technique has not been previously used to detect and quantify juice-to-juice adulterations.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data

Calle

Barea-Sepúlveda

Ruiz-Rodríguez

et al. 2022

Sensors

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Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices.

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Quality assessment of fruit juices by NIR spectroscopy

Abstract: AbstractŠNURKOVIČ PETR: Quality assessment of fruit juices by NIR spectroscopy. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2013, LXI, No. 3, pp. 803-812

Cited by 11 publications

References 18 publications

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning

Non-Invasive Methods for Predicting the Quality of Processed Horticultural Food Products, with Emphasis on Dried Powders, Juices and Oils: A Review

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data

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