1
|
Millatina NRN, Calle JLP, Barea-Sepúlveda M, Setyaningsih W, Palma M. Detection and quantification of cocoa powder adulteration using Vis-NIR spectroscopy with chemometrics approach. Food Chem 2024; 449:139212. [PMID: 38583399 DOI: 10.1016/j.foodchem.2024.139212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/12/2024] [Accepted: 03/31/2024] [Indexed: 04/09/2024]
Abstract
The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of adulteration practices aimed at achieving economic benefits. This study aimed to detect and quantify cocoa powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat. The NIRS data could not be resolved using Savitzky-Golay smoothing. Nevertheless, the application of a random forest and support vector machine successfully classified the samples with 100% accuracy. Quantification of adulteration using partial least squares (PLS), Lasso, Ridge, elastic Net, and RF regressions provided R2 higher than 0.96 and root mean square error <2.6. Coupling PLS with the Boruta algorithm produced the most reliable regression model (R2 = 1, RMSE = 0.0000). Finally, an online application was prepared to facilitate the determination of adulterants in the cocoa powder.
Collapse
Affiliation(s)
- Nela Rifda Nur Millatina
- Department of Food and Agricultural Product Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jalan Flora, Bulaksumur, 55281 Yogyakarta, Indonesia
| | - José Luis Pérez Calle
- Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, Campus de Excelencia Internacional Agroalimentario (CeiA3), Campus del Rio San Pedro, 11510, Puerto Real, Cádiz, Spain
| | - Marta Barea-Sepúlveda
- Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, Campus de Excelencia Internacional Agroalimentario (CeiA3), Campus del Rio San Pedro, 11510, Puerto Real, Cádiz, Spain
| | - Widiastuti Setyaningsih
- Department of Food and Agricultural Product Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jalan Flora, Bulaksumur, 55281 Yogyakarta, Indonesia..
| | - Miguel Palma
- Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, Campus de Excelencia Internacional Agroalimentario (CeiA3), Campus del Rio San Pedro, 11510, Puerto Real, Cádiz, Spain
| |
Collapse
|
2
|
Shiv K, Singh A, Kumar S, Prasad LB, Gupta S, Bharty MK. Evaluation of different regression models for detection of adulteration of mustard and canola oil with argemone oil using fluorescence spectroscopy coupled with chemometrics. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2024; 41:105-119. [PMID: 38180769 DOI: 10.1080/19440049.2023.2297869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/10/2023] [Indexed: 01/06/2024]
Abstract
Mustard and canola oils are commonly used cooking oils in Asian countries such as India, Nepal, and Bangladesh, making them prone to adulteration. Argemone is a well-known adulterant of mustard oil, and its alkaloid sanguinarine has been linked with health conditions such as glaucoma and dropsy. Utilising a non-destructive spectroscopic method coupled with a chemometric approach can serve better for the detection of adulterants. This work aimed to evaluate the performance of various regression algorithms for the detection of argemone in mustard and canola oils. The spectral dataset was acquired from fluorescence spectrometer analysis of pure as well as adulterated mustard and canola oils with some local and commercial samples also. The prediction performance of the eight regression algorithms for the detection of adulterants was evaluated. Extreme gradient boosting regressor (XGBR), Category gradient boosting regressor (CBR), and Random Forest (RF) demonstrate potential for predicting adulteration levels in both oils with high R2 values.
Collapse
Affiliation(s)
- Kunal Shiv
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Anupam Singh
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Sachin Kumar
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Lal Bahadur Prasad
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Seema Gupta
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Manoj Kumar Bharty
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| |
Collapse
|
3
|
Calle JLP, Vázquez-Espinosa M, Barea-Sepúlveda M, Ruiz-Rodríguez A, Ferreiro-González M, Palma M. Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices. Foods 2023; 12:2536. [PMID: 37444273 DOI: 10.3390/foods12132536] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 06/26/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Fruit juices are one of the most widely consumed beverages worldwide, and their production is subject to strict regulations. Therefore, this study presents a methodology based on the use of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) in combination with machine-learning algorithms for the characterization juices of different raw material (orange, pineapple, or apple and grape). For this purpose, the ion mobility sum spectrum (IMSS) was used. First, an optimization of the most important conditions in generating the HS was carried out using a Box-Behnken design coupled with a response surface methodology. The following factors were studied: temperature, time, and sample volume. The optimum values were 46.3 °C, 5 min, and 750 µL, respectively. Once the conditions were optimized, 76 samples of the different types of juices were analyzed and the IMSS was combined with different machine-learning algorithms for its characterization. The exploratory analysis by hierarchical cluster analysis (HCA) and principal component analysis (PCA) revealed a clear tendency to group the samples according to the type of fruit juice and, to a lesser extent, the commercial brand. The combination of IMSS with supervised classification techniques reported an excellent result with 100% accuracy on the test set for support vector machines (SVM) and random forest (RF) models regarding the specific fruit used. Nevertheless, all the models have proven to be an effective alternative for characterizing and classifying the different types of juices.
Collapse
Affiliation(s)
- José Luis P Calle
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real, 11510 Cadiz, Spain
| | - Mercedes Vázquez-Espinosa
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real, 11510 Cadiz, Spain
| | - Marta Barea-Sepúlveda
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real, 11510 Cadiz, Spain
| | - Ana Ruiz-Rodríguez
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real, 11510 Cadiz, Spain
| | - Marta Ferreiro-González
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real, 11510 Cadiz, Spain
| | - Miguel Palma
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real, 11510 Cadiz, Spain
| |
Collapse
|
4
|
Calle JLP, Punta-Sánchez I, González-de-Peredo AV, Ruiz-Rodríguez A, Ferreiro-González M, Palma M. Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning. Foods 2023; 12:2491. [PMID: 37444229 DOI: 10.3390/foods12132491] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/20/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- José Luis P Calle
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain
| | - Irene Punta-Sánchez
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain
| | - Ana Velasco González-de-Peredo
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain
| | - Ana Ruiz-Rodríguez
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain
| | - Marta Ferreiro-González
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain
| | - Miguel Palma
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain
| |
Collapse
|
5
|
Artificial Intelligence Aided Adulteration Detection and Quantification for Red Chilli Powder. FOOD ANAL METHOD 2023. [DOI: 10.1007/s12161-023-02445-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
|