Comparison of Portable and Benchtop Near-Infrared Spectrometers for the Detection of Citric Acid-adulterated Lime Juice: A Chemometrics Approach

Jahani, Reza; Ruth, Saskia M. van; Weesepoel, Yannick; Alewijn, Martin; Kobarfard, Farzad; Faizi, Mehrdad; Shojaei, Mohammad Hossain; Mahboubi, Arash; Nasiri, Azadeh; Yazdanpanah, Hassan

doi:10.5812/ijpr-128372

Cited by 7 publications

(9 citation statements)

References 55 publications

(56 reference statements)

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“…Currently, analytical separation techniques, including both liquid and gas chromatography, are the most widely used methodologies for the detection of adulterants in juices [6,8,[10][11][12][13]. However, other techniques such as isotope-based techniques and elemental techniques [14][15][16], physicochemical analysis [17,18], DNA-based techniques [19,20], and spectroscopic techniques [6,8,12,13,21] have been employed. Among them, spectroscopic techniques are becoming more and more important due to their numerous advantages such as low analysis time and costs, ease of use, high reproducibility, and greater portability, allowing in situ analysis.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, Fourier transform infrared spectroscopy (FT-IR) has been one of the most extensive techniques to successfully detect adulterations in juices. It has been employed for the detection of sugar addition in apple [22][23][24], mango [25], and orange [12] juices, the authentication of different commercial juices [26], the detection of grape juice in pomegranate juice [27], the discrimination of lime juice adulterated with isocitric and/or citric acid [21], as well as the authentication of Concord grape juice in grape blends [10].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data

et al. 2022

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Fruit juices are one of the most adulterated beverages, usually because of the addition of water, sugars, or less expensive fruit juices. This study presents a method based on Fourier transform infrared spectroscopy (FT-IR), in combination with machine learning methods, for the correct identification and quantification of adulterants in juices. Thus, three types of 100% squeezed juices (pineapple, orange, and apple) were evaluated and adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The results of the exploratory data analysis revealed a clear clustering trend of the samples according to the type of juice analyzed. The supervised learning analysis, based on the development of models for the detection of adulteration, obtained significant results for all tested methods (i.e., support-vector machines or SVM), random forest or RF, and linear discriminant analysis or LDA) with an accuracy above 97% on the test set. Regarding quantification, the best results are obtained with the support vector regression and with partial least square regression showing an R2 greater than 0.99 and a root mean square error (RMSE) less than 1.4 for the test set.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data

et al. 2022

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“…Recently, portable and handheld NIR devices have drawn increasing attention in the food industry as online quality monitoring tools; however, their application in quality control of fruit juice and wine remains very limited. A handheld NIR spectrophotometer was applied by Jahani et al [61] who achieved classification correction of 88% in detecting adulterated lime juices using PLS-DA method with SNV transforming. Additionally, Ehsani et al [75] obtained the accuracy of 100% in authenticating orange juices using handheld NIR spectrometers.…”

Section: Infrared (Ir) Spectroscopic Techniquementioning

confidence: 99%

Current Techniques for Fruit Juice and Wine Adulterant Detection and Authentication

Mac,

Pham,

et al. 2023

Beverages

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Fruit juice and wine are important beverages that are consumed all over the world. Due to their constantly increasing demand and high value, fruit juice and wine are one of the most frequent targets of adulteration. Since adulterated foods are proven to have harmful effects on health, several approaches have been utilized for the detection of fruit juice and wine adulteration. Based on the requirement for sample destruction, analytical techniques to assess food authenticity can be classified into 2 main categories, i.e., destructive and non-destructive techniques. This paper provides an overview on the principle of adulteration detection, its application and performance, and the advantages and limitations of various analytical techniques. Destructive approaches, such as physicochemical methods, isotope analysis, elemental analysis, chromatographic techniques, and DNA-based techniques, are reviewed. Furthermore, non-destructive approaches, including spectroscopic-based techniques, nuclear magnetic resonance spectroscopic technique, electronic techniques, and imaging-based techniques, are discussed.

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“…28,29 Portable and handheld devices with very compact dimensions and low price connected to a smartphone via Bluetooth, operated easily to evaluate common food, such as dairy food, fruits, vegetables, spices or meat products. [30][31][32][33][34] In spite of the vast profits of portable spectrometers, most of such pocket-sized devices have low resolution and are less sensitive compared to benchtop types. Therefore, a combination of such low resolution devices with chemometric techniques is an effective tool to enhance both sensitivity, and accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Such advantages make portable spectrometers suitable in the characterization of food products and screening of food quality and authenticity, reflecting the applicability of this technology to conventional spectroscopy that is limited to laboratory use 28,29 . Portable and handheld devices with very compact dimensions and low price connected to a smartphone via Bluetooth, operated easily to evaluate common food, such as dairy food, fruits, vegetables, spices or meat products 30–34 . In spite of the vast profits of portable spectrometers, most of such pocket‐sized devices have low resolution and are less sensitive compared to benchtop types.…”

Section: Introductionmentioning

confidence: 99%

Ensemble classification and regression techniques combined with portable near infrared spectroscopy for facile and rapid detection of water adulteration in bovine raw milk

Ehsani

Dastgerdy

Yazdanpanah

et al. 2022

Journal of Chemometrics

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The objective of the present contribution is to use a portable near infrared (NIR) spectrometer (in spectral range of 900-1700 nm) as a rapid, facile, and nondestructive technique in combination with ensemble methods, for detection of water in bovine milk samples in concentration range 1% to 30% (v/v).On this matter, the pattern of the milk samples (pure and adulterated) was explored by principal component analysis (PCA). Then, random subspace discriminant ensemble (RSDE) was used for classification. The classification figures of merit for the RSDE method was evaluated in terms of sensitivity (Sen), specificity (Spe), accuracy (Acc), error rate (ER), and reliability (Rel). All the values were satisfactory for calibration, cross-validation, and prediction sets, which showed the reliability and robustness of the developed model. Furthermore, the performance of RSDE method was compared with partial least square-discriminant analysis (PLS-DA) and support vector machine (SVM) as the most common classification techniques. In overall, the RSDE classification outperformed the other tested classification methods (PLS-DA and RBF-SVM) in terms of accuracy and reliability. Finally, boosted regression tree (BRT) was used for quantifying the level of water adulterant in milk. The performance of the ensemble regression model was evaluated using regression coefficient (R 2 ) and root mean square error (RMSE). The values of R 2 and RMSE in prediction set for BRT were 0.95 and 0.58, respectively. The performance of BRT method was also compared with partial least squares regression (PLSR) which again showed outperformance in comparison with this frequent used regression technique.

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Comparison of Portable and Benchtop Near-Infrared Spectrometers for the Detection of Citric Acid-adulterated Lime Juice: A Chemometrics Approach

Cited by 7 publications

References 55 publications

Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data

Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data

Current Techniques for Fruit Juice and Wine Adulterant Detection and Authentication

Ensemble classification and regression techniques combined with portable near infrared spectroscopy for facile and rapid detection of water adulteration in bovine raw milk

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