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Okolo CA, Kilcawley KN, O'Connor C. Recent advances in whiskey analysis for authentication, discrimination, and quality control. Compr Rev Food Sci Food Saf 2023; 22:4957-4992. [PMID: 37823807 DOI: 10.1111/1541-4337.13249] [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: 03/23/2023] [Revised: 08/29/2023] [Accepted: 09/12/2023] [Indexed: 10/13/2023]
Abstract
In order to safeguard authentic whiskey products from fraudulent or counterfeit practices, high throughput solutions that provide robust, rapid, and reliable solutions are required. The implementation of some analytical strategies is quite challenging or costly in routine analysis. Qualitative screening of whiskey products has been explored, but due to the nonspecificity of the chemical compounds, a more quantitative confirmatory technique is required to validate the result of the whiskey analysis. Hence, combining analytical and chemometric methods has been fundamental in whiskey sample differentiation and classification. A comprehensive update on the most relevant and current analytical techniques, including spectroscopic, chromatographic, and novel technologies employed within the last 5 years in whiskey analysis for authentication, discrimination, and quality control, are presented. Furthermore, the technical challenges in employing these analytical techniques, future trends, and perspectives are emphasized.
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Affiliation(s)
- Chioke A Okolo
- FOCAS Research Institute, Technological University Dublin, Dublin, Ireland
- School of Food Science & Environmental Health, Technological University Dublin, Dublin, Ireland
| | - Kieran N Kilcawley
- Food Quality & Sensory Science Department, Teagasc Food Research Centre, Co Cork, Ireland
- School of Food and Nutritional Sciences, College of Science, Engineering and Food Science, University College Cork, Cork, Ireland
| | - Christine O'Connor
- School of Food Science & Environmental Health, Technological University Dublin, Dublin, Ireland
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Revelation for the Influence Mechanism of Long-Chain Fatty Acid Ethyl Esters on the Baijiu Quality by Multicomponent Chemometrics Combined with Modern Flavor Sensomics. Foods 2023; 12:foods12061267. [PMID: 36981194 PMCID: PMC10048143 DOI: 10.3390/foods12061267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/02/2023] [Accepted: 03/12/2023] [Indexed: 03/19/2023] Open
Abstract
Long-chain fatty acid ethyl ester (LCFAEEs) is colorless and has a weak wax and cream aroma. It can be used as an intermediate for the synthesis of emulsifiers, and stabilizers and be applied in the production of flavor essence. It is also an important trace component in Baijiu and is attributed to making a contribution to the quality of Baijiu, but its distribution in Baijiu has not been clear, and its influence mechanisms on Baijiu quality have not been systematically studied. Therefore, the distribution of LCFAEEs for Baijiu in different years (2014, 2015, 2018, and 2022), different grades (premium, excellent, and level 1; note: here Baijiu grade classification was based on Chinese standard (GB/T 10781) and enterprise classification standard), and different sun exposure times (0, 6, 12, 20, 30, and 50 days) was uncovered. Thus, in this study, the effect of LCFAEEs on the quality of Baijiu was comprehensively and objectively proven by combining modern flavor sensomics and multicomponent chemometrics. The results showed that with the increase in Baijiu storage time, the concentration of LCFAEEs increased significantly in Baijiu (4.38–196.95 mg/L, p < 0.05). The concentration of LCFAEEs in level 1 Baijiu was significantly higher than that in excellent and premium Baijiu (the concentration ranges of ET, EP, EO, E9, E912, and E91215 were: 0.27–2.31 mg/L, 0.75–47.41 mg/L, 0.93–1.80 mg/L, 0.98–12.87 mg/L, 1.01–27.08 mg/L, and 1.00–1.75 mg/L, respectively, p < 0.05). With the increase in sun exposure time, the concentration of LCFAEEs in the Baijiu first increased significantly and then decreased significantly (4.38–5.95 mg/L, p < 0.05). As the flavor sensomics showed, the concentrations of LCFAEEs in Baijiu bodies were significantly correlated with the Baijiu taste sense (inlet taste, aroma sensation in the mouth), as well as with the evaluation after drinking (maintaining taste) (p < 0.05, r > 0.7). Based on the above, LCFAEEs are critical factors for Baijiu flavor thus, it is essential to explore a suitable concentration of LCFAEEs in Baijiu to make Baijiu’s quality more ideal.
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Food Fraud Vulnerability Assessment in the Chinese Baijiu Supply Chain. Foods 2023; 12:foods12030516. [PMID: 36766045 PMCID: PMC9914212 DOI: 10.3390/foods12030516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/14/2023] [Accepted: 01/16/2023] [Indexed: 01/25/2023] Open
Abstract
As a representative of Chinese alcoholic drinks, baijiu has developed into a mass-consumption commodity. Its simple industrial chain makes it a suitable target for fraudsters. In order to understand the differences and potential factors of fraud vulnerability among groups at various levels, this study constructed a food fraud vulnerability assessment system for the Chinese baijiu supply chain based on routine activities theory. We examined the fraud vulnerability in the baijiu supply chain with data from 243 producers and 45 retailers by using the safe supply of affordable food everywhere (SSAFE) food fraud vulnerability assessment (FFVA) tool. The results indicate that fraud factors related to opportunities have an overall medium vulnerability, while those related to motivations and control measures have an overall medium-low vulnerability. In addition, there are significant differences in the perceived vulnerability of fraud factors across the supply chain. Moreover, retailers have overall higher fraud vulnerability in terms of opportunities and control measures than producers. The main reasons for the frequent occurrence of fraud in the baijiu industry are numerous technical opportunities, strong economic drivers, and insufficient control measures.
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Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:8655. [PMID: 36433249 PMCID: PMC9697730 DOI: 10.3390/s22228655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice's weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94-0.98) and non-invasive measurement through the packaging (NIR; R = 0.95-0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain.
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Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
- Department of Agrotechnology, Faculty of Mechanical Engineering and Technology, University Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
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Scappaticci C, Spera S, Biancolillo A, Marini F. Detection and Quantification of Alprazolam Added to Long Drinks by Near Infrared Spectroscopy and Chemometrics. Molecules 2022; 27:molecules27196420. [PMID: 36234957 PMCID: PMC9572568 DOI: 10.3390/molecules27196420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/17/2022] [Accepted: 09/26/2022] [Indexed: 11/28/2022] Open
Abstract
In the present work, a fast, relatively cheap, and green analytical strategy to identify and quantify the fraudulent (or voluntary) addition of a drug (alprazolam, the API of Xanax®) to an alcoholic drink of large consumption, namely gin and tonic, was developed using coupling near-infrared spectroscopy (NIR) and chemometrics. The approach used was both qualitative and quantitative as models were built that would allow for highlighting the presence of alprazolam with high accuracy, and to quantify its concentration with, in many cases, an acceptable error. Classification models built using partial least squares discriminant analysis (PLS-DA) allowed for identifying whether a drink was spiked or not with the drug, with a prediction accuracy in the validation phase often higher than 90%. On the other hand, calibration models established through the use of partial least squares (PLS) regression allowed for quantifying the drug added with errors of the order of 2–5 mg/L.
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Affiliation(s)
- Claudia Scappaticci
- Department of Chemistry, University of Rome “La Sapienza”, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Stella Spera
- Department of Chemistry, University of Rome “La Sapienza”, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Alessandra Biancolillo
- Department of Physical and Chemical Sciences, University of L’Aquila, Via Vetoio, 67100 L’Aquila, Italy
| | - Federico Marini
- Department of Chemistry, University of Rome “La Sapienza”, Piazzale Aldo Moro 5, 00185 Rome, Italy
- Correspondence:
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Yang Z, Zhou Q, Wu W, Zhang D, Mo L, Liu J, Yang X. Food fraud vulnerability assessment in the edible vegetable oil supply chain: A perspective of Chinese enterprises. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Chen MJ, Yin HL, Liu Y, Wang RR, Jiang LW, Li P. Non-destructive prediction of the hotness of fresh pepper with a single scan using portable near infrared spectroscopy and a variable selection strategy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:114-124. [PMID: 34913444 DOI: 10.1039/d1ay01634b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
There has been no study on using near-infrared spectroscopy (NIRS) to predict the hotness of fresh pepper. This study is aimed at developing a non-destructive and accurate method for determining the hotness of fresh peppers using portable NIRS and the variable selection strategy. Spectra from different locations on samples were obtained non-destructively with a single scan. Quantitative models were established using partial least squares (PLS) with a variable selection method or fusion method. The results showed that near-stalk was the best spectral acquisition location for quantitative analysis. The variable selection strategy allows the selection of targeted characteristic variables and improves the results. A fusion method, namely variable adaptive boosting partial least squares (VABPLS), was selected for optimal prediction of the performance. In the optimized model, the root mean square errors of prediction for the validation set (RMSEPvs) of capsaicin, dihydrocapsaicin and pungency degree were 0.295, 0.143 and 47.770, respectively, while the root mean square errors of prediction for the prediction set (RMSEPps) collected one month later were 0.273, 0.346 and 75.524, respectively.
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Affiliation(s)
- Meng-Juan Chen
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Han-Liang Yin
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Yang Liu
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Rong-Rong Wang
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Li-Wen Jiang
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Pao Li
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
- Hunan Agricultural Product Processing Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, P. R. China
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Assessment of Wine Adulteration Using Near Infrared Spectroscopy and Laser Backscattering Imaging. Processes (Basel) 2022. [DOI: 10.3390/pr10010095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Food adulteration is in the focus of research due to its negative effect on safety and nutritional value and because of the demand for the protection of brands and regional origins. Portugieser and Sauvignon Blanc wines were selected for experiments. Samples were made by water dilution, the addition of sugar and then a combination of both. Near infrared (NIR) spectra were acquired in the range of 900–1700 nm. Partial least squares regression was performed to predict the adulteration level. The model including all wines and adulterations achieved a prediction error of 0.59% added sugar and 6.85% water dilution. Low-power laser modules were used to collect diffuse reflectance signals at wavelengths of 532, 635, 780, 808, 850, 1064 nm. The general linear model resulted in a higher prediction error of 3.06% added sugar and 20.39% water dilution. Instead of classification, the present study investigated the feasibility of non-destructive methods in the prediction of adulteration level. Laser scattering successfully detected the added sugar with linear discriminant analysis (LDA), but its prediction accuracy was low. NIR spectroscopy might be suitable for rapid non-destructive estimation of wine adulteration.
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PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy. SENSORS 2021; 22:s22010286. [PMID: 35009831 PMCID: PMC8749750 DOI: 10.3390/s22010286] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 12/24/2021] [Accepted: 12/29/2021] [Indexed: 11/30/2022]
Abstract
Near-infrared spectroscopic (NIR) technique was used, for the first time, to predict volatile phenols content, namely guaiacol, 4-methyl-guaiacol, eugenol, syringol, 4-methyl-syringol and 4-allyl-syringol, of aged wine spirits (AWS). This study aimed to develop calibration models for the volatile phenol’s quantification in AWS, by NIR, faster and without sample preparation. Partial least square regression (PLS-R) models were developed with NIR spectra in the near-IR region (12,500–4000 cm−1) and those obtained from GC-FID quantification after liquid-liquid extraction. In the PLS-R developed method, cross-validation with 50% of the samples along a validation test set with 50% of the remaining samples. The final calibration was performed with 100% of the data. PLS-R models with a good accuracy were obtained for guaiacol (r2 = 96.34; RPD = 5.23), 4-methyl-guaiacol (r2 = 96.1; RPD = 5.07), eugenol (r2 = 96.06; RPD = 5.04), syringol (r2 = 97.32; RPD = 6.11), 4-methyl-syringol (r2 = 95.79; RPD = 4.88) and 4-allyl-syringol (r2 = 95.97; RPD = 4.98). These results reveal that NIR is a valuable technique for the quality control of wine spirits and to predict the volatile phenols content, which contributes to the sensory quality of the spirit beverages.
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Ahmad I, Sadiq MB, Liu A, Benjamin TA, Gump BH. Effect of Low-Frequency Ultrasonication on Red Wine Astringency. JOURNAL OF CULINARY SCIENCE & TECHNOLOGY 2021. [DOI: 10.1080/15428052.2021.2002228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Imran Ahmad
- Food, Agriculture and Bio-innovation Laboratories, Chaplin School of Hospitality and Tourism Management, Florida International University (Biscayne Bay Campus), North Miami, Florida, USA
| | - Muhammad B. Sadiq
- School of Life Sciences, Forman Christian College (A Chartered University), Lahore, Pakistan
| | - A. Liu
- Food, Agriculture and Bio-innovation Laboratories, Chaplin School of Hospitality and Tourism Management, Florida International University (Biscayne Bay Campus), North Miami, Florida, USA
| | - T-A. Benjamin
- Food, Agriculture and Bio-innovation Laboratories, Chaplin School of Hospitality and Tourism Management, Florida International University (Biscayne Bay Campus), North Miami, Florida, USA
| | - Barry H. Gump
- Brew Science Program, Chaplin School of Hospitality and Tourism Management (FIU Brew Lab), Florida International University (Biscayne Bay Campus), North Miami, Florida USA
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