Novel analytical method for detection of orange juice adulteration based on ultra-fast gas chromatography

Różańska, Anna; Dymerski, Tomasz; Namieśnik, Jacek

doi:10.1007/s00706-018-2233-8

Cited by 24 publications

(12 citation statements)

References 42 publications

(41 reference statements)

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“…The juiciness is measured as the area stained by the juice. That will in turn correlate to mealiness and woolliness [97]. The process is simple and easy to implement, but the expert opinion is that dependency is the main challenge for this technique [98].…”

Section: Detection Methods Based On Quantity Of Free Juicementioning

confidence: 99%

A Review on the Commonly Used Methods for Analysis of Physical Properties of Food Materials

et al. 2022

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The chemical composition of any food material can be analyzed well by employing various analytical techniques. The physical properties of food are no less important than chemical composition as results obtained from authentic measurement data are able to provide detailed information about the food. Several techniques have been used for years for this purpose but most of them are destructive in nature. The aim of this present study is to identify the emerging techniques that have been used by different researchers for the analysis of the physical characteristics of food. It is highly recommended to practice novel methods as these are non-destructive, extremely sophisticated, and provide results closer to true quantitative values. The physical properties are classified into different groups based on their characteristics. The concise view of conventional techniques mostly used to analyze food material are documented in this work.

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Section: Detection Methods Based On Quantity Of Free Juicementioning

confidence: 99%

A Review on the Commonly Used Methods for Analysis of Physical Properties of Food Materials

et al. 2022

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“…Comparing different classification methods as HCA, classification tree, naive Bayes, ANN, and random forest classifiers. Random Forest classified the orange juice samples with almost 100% accuracy [ 175 ].…”

Section: Electronic Nose: Historical Background and Food Quality Amentioning

confidence: 99%

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview

Aouadi

Zaukuu

Vitális

et al. 2020

Sensors

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Amid today’s stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

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“…This illegal practice is carried out by some manufacturers to satisfy the high demand for the product, to hide low-quality raw materials, and to obtain higher economic benefits [ 6 ]. Juice-to-juice adulterations are very popular because they are complex matrices that are difficult to detect, which makes them very sophisticated [ 7 ], and grape juice is one of the most used for adulteration due to its low cost [ 8 ].…”

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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Novel analytical method for detection of orange juice adulteration based on ultra-fast gas chromatography

Cited by 24 publications

References 42 publications

A Review on the Commonly Used Methods for Analysis of Physical Properties of Food Materials

A Review on the Commonly Used Methods for Analysis of Physical Properties of Food Materials

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview

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

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