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Zong X, Zhou X, Cao X, Gao S, Zhang D, Zhang H, Qiu R, Wang Y, Wu J, Li L. Quantitative modelling of Plato and total flavonoids in Qingke wort at mashing and boiling stages based on FT-IR combined with deep learning and chemometrics. Food Chem X 2024; 23:101673. [PMID: 39148529 PMCID: PMC11324842 DOI: 10.1016/j.fochx.2024.101673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 06/29/2024] [Accepted: 07/17/2024] [Indexed: 08/17/2024] Open
Abstract
Craft beer brewers need to learn process control strategies from traditional industrial production to ensure the consistent quality of the finished product. In this study, FT-IR combined with deep learning was used for the first time to model and analyze the Plato degree and total flavonoid content of Qingke beer during the mashing and boiling stages and to compare the effectiveness with traditional chemometrics methods. Two deep learning neural networks were designed, the effect of variable input methods on the effectiveness of the models was discussed. The experimental results showed that the CARS-LSTM model had the best predictive performance, not only as the best quantitative model for Plato in the mashing (R2p = 0.9368) and boiling (R2p = 0.9398) phases but also as the best model for TFC in the boiling phase (R2p = 0.9154). This study demonstrates the great potential of deep learning and provides a new approach to quality control analysis in beer brewing.
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Affiliation(s)
- Xuyan Zong
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
- College of Bioengineering, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
| | - Xianjiang Zhou
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
- College of Bioengineering, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
| | - Xinyue Cao
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
- College of Bioengineering, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
| | - Shun Gao
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
- College of Bioengineering, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
| | - Dongyang Zhang
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
- College of Bioengineering, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
| | - Haoran Zhang
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
- College of Bioengineering, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
| | - Ran Qiu
- China Resources Snow Breweries Co., Ltd, Beijing, 100000, China
| | - Yi Wang
- Wuliangye Group Co., Ltd, Yibin, 644000, Sichuan, China
| | - Jianhang Wu
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
- College of Bioengineering, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
| | - Li Li
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
- College of Bioengineering, Sichuan University of Science and Engineering, Yibin, 644000, Sichuan, China
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Trojanowski B, Strzelak K, Koncki R. Multipoint monitor of beer fermentation. Food Chem 2024; 452:139613. [PMID: 38744125 DOI: 10.1016/j.foodchem.2024.139613] [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: 02/08/2024] [Revised: 04/25/2024] [Accepted: 05/07/2024] [Indexed: 05/16/2024]
Abstract
This short communication is devoted to a fully-mechanized flow analysis system for the control of beer fermentation process. The developed system is based on microsolenoid flow controlling devices (valves and pumps) and a flow-through optoelectronic detector. All these components are powered and controlled by a Adruino-compatible microprocessor platform that creates an integrated, compact, and robust analytical tool. Multiplication of sample aspiration ports of the analytical system allows for simultaneous monitoring of several independently performed fermentation processes, as well as a single process at the different places of fermentation tank. To demonstrate its practical utility, the developed system has been applied for online and real-time monitoring of yeast propagation and distribution in beer worts in the course of various fermentation processes. Potentially, this flow analysis system can be easily expanded to the form of multianalyte monitor equipped with optoelectronic sensors and biosensors for the determination of other parameters and analytes.
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Affiliation(s)
| | - Kamil Strzelak
- Faculty of Chemistry, University of Warsaw, Warsaw, Poland.
| | - Robert Koncki
- Faculty of Chemistry, University of Warsaw, Warsaw, Poland
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Tanzilli D, Cocchi M, Amigo JM, D'Alessandro A, Strani L. Does hyperspectral always matter? A critical assessment of near infrared versus hyperspectral near infrared in the study of heterogeneous samples. Curr Res Food Sci 2024; 9:100813. [PMID: 39149525 PMCID: PMC11325669 DOI: 10.1016/j.crfs.2024.100813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/04/2024] [Accepted: 07/18/2024] [Indexed: 08/17/2024] Open
Abstract
Near Infrared spectroscopy (NIR), in combination with Chemometrics, has been used for many years in diverse scenarios, mostly focused on the classification and quantitation of properties in food, pharmaceutical preparations, artwork material, etc. This success has been possible due to their desirable properties: fast, reliable (under certain conditions), non-destructive, easy to implement from a hardware perspective, and able to create robust and transferable multivariate models. For some years now, another modality has been gaining the attention of NIR users, especially in the Food sector. That is the plausibility of using NIR in the hyperspectral (HSI) domain. This adds to the previously mentioned abilities, the benefit of scanning the whole surface of samples, acquiring much richer spatial information and, therefore, assuring the quality of the final product more accurately by including parameters that depend on the surface distribution of certain components. This is especially relevant in heterogeneous samples. While this statement is generally true, there are certain situations where this oversampling feature is not strictly needed, and the problem can be easily solved with a classical NIR spectrophotometer. Besides, NIR-hyperspectral imaging (NIR-HSI), despite the abovementioned advantages, has several drawbacks that must be highlighted as well, like their measuring speed, instability, or price. This manuscript will demonstrate that for certain situations, tuning the focal distance of a NIR spectrophotometer is a more feasible, reliable, and inexpensive strategy to collect all the needed information of samples with a certain degree of heterogeneity.
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Affiliation(s)
- Daniele Tanzilli
- Dipartimento di Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, Via Campi 103, 41125, Modena, Italy
- University of Lille, LASIRE, Cité Scientifique, Villeneuve-d'Ascq, 59650, France
| | - Marina Cocchi
- Dipartimento di Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - José Manuel Amigo
- IKERBASQUE, Basque Society for the Promotion of Science, Plaza Euskadi, 5, Bilbao, 48009, Spain
- Department of Analytical Chemistry, University of the Basque Country, Barrio Sarriena S/N, Leioa, 48940, Spain
| | - Alessandro D'Alessandro
- Dipartimento di Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, Via Campi 103, 41125, Modena, Italy
- Barilla G. and R. Fratelli, via Mantova 166, 43122, Parma, Italy
| | - Lorenzo Strani
- Dipartimento di Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, Via Campi 103, 41125, Modena, Italy
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Zhou X, Li L, Zheng J, Wu J, Wen L, Huang M, Ao F, Luo W, Li M, Wang H, Zong X. Quantitative analysis of key components in Qingke beer brewing process by multispectral analysis combined with chemometrics. Food Chem 2024; 436:137739. [PMID: 37839128 DOI: 10.1016/j.foodchem.2023.137739] [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: 07/10/2023] [Revised: 09/16/2023] [Accepted: 10/10/2023] [Indexed: 10/17/2023]
Abstract
In order to monitor the Qingke beer brewing process in real time, this paper presents an analytical method for predicting the content of key components in the wort during the mashing and boiling stages using multi-spectroscopy combined with chemometrics. The results showed that the Neural Networks (NN) model based on Raman spectroscopy (RPD = 3.9727) and the NN model based on NIR spectroscopy (RPD = 5.1952) had the best prediction performance for the reducing sugar content in the mashing and boiling stages; The partial least Squares (PLS) model based on Raman spectroscopy (RPD = 2.7301) and the NN model based on Raman spectroscopy (RPD = 4.3892) predicted the content of free amino nitrogen best; The PLS model based on UV-Vis spectroscopy (RPD = 4.0412) and the NN model based on Raman spectroscopy (RPD = 4.0540) are most suitable for the quantitative analysis of total phenols. The results can be used as a guide for real-time control of wort quality in industrial production.
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Affiliation(s)
- Xianjiang Zhou
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Li Li
- College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Jia Zheng
- Wuliangye Group Co., Ltd, Yibin 644000, Sichuan, China.
| | - Jianhang Wu
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Lei Wen
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Min Huang
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Feng Ao
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Wenli Luo
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Mao Li
- Wuliangye Group Co., Ltd, Yibin 644000, Sichuan, China.
| | - Hong Wang
- Wuliangye Group Co., Ltd, Yibin 644000, Sichuan, China.
| | - Xuyan Zong
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
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Xin X, Jia J, Pang S, Hu R, Gong H, Gao X, Ding X. Combination of near-infrared spectroscopy with Wasserstein generative adversarial networks for rapidly detecting raw material quality for formula products. OPTICS EXPRESS 2024; 32:5529-5549. [PMID: 38439277 DOI: 10.1364/oe.516341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 01/19/2024] [Indexed: 03/06/2024]
Abstract
Near-infrared spectroscopy (NIRS) has emerged as a key technique for rapid quality detection owing to its fast, non-destructive, and eco-friendly characteristics. However, its practical implementation within the formulation industry is challenging owing to insufficient data, which renders model fitting difficult. The complexity of acquiring spectra and spectral reference values results in limited spectral data, aggravating the problem of low generalization, which diminishes model performance. To address this problem, we introduce what we believe to be a novel approach combining NIRS with Wasserstein generative adversarial networks (WGANs). Specifically, spectral data are collected from representative samples of raw material provided by a formula enterprise. Then, the WGAN augments the database by generating synthetic data resembling the raw spectral data. Finally, we establish various prediction models using the PLSR, SVR, LightGBM, and XGBoost algorithms. Experimental results show the NIRS-WGAN method significantly improves the performance of prediction models, with R2 and RMSE of 0.949 and 1.415 for the chemical components of sugar, respectively, and 0.922 and 0.243 for nicotine. The proposed framework effectively enhances the predictive capabilities of various models, addressing the issue caused by limited training data in NIRS prediction tasks.
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Tanzilli D, D'Alessandro A, Tamelli S, Durante C, Cocchi M, Strani L. A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics. Foods 2023; 12:foods12081679. [PMID: 37107474 PMCID: PMC10137520 DOI: 10.3390/foods12081679] [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: 03/15/2023] [Revised: 04/10/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
The food industry needs tools to improve the efficiency of their production processes by minimizing waste, detecting timely potential process issues, as well as reducing the efforts and workforce devoted to laboratory analysis while, at the same time, maintaining high-quality standards of products. This can be achieved by developing on-line monitoring systems and models. The present work presents a feasibility study toward establishing the on-line monitoring of a pesto sauce production process by means of NIR spectroscopy and chemometric tools. The spectra of an intermediate product were acquired on-line and continuously by a NIR probe installed directly on the process line. Principal Component Analysis (PCA) was used both to perform an exploratory data analysis and to build Multivariate Statistical Process Control (MSPC) charts. Moreover, Partial Least Squares (PLS) regression was employed to compute real time prediction models for two different pesto quality parameters, namely, consistency and total lipids content. PCA highlighted some differences related to the origin of basil plants, the main pesto ingredient, such as plant age and supplier. MSPC charts were able to detect production stops/restarts. Finally, it was possible to obtain a rough estimation of the quality of some properties in the early production stage through PLS.
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Affiliation(s)
- Daniele Tanzilli
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
- Université de Lille, CNRS, LASIRE (UMR 8516), Laboratoire Avancé de Spectroscopie pour les Interactions, la Réactivité et l'Environnement, 59000 Lille, France
| | - Alessandro D'Alessandro
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Samuele Tamelli
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Caterina Durante
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Marina Cocchi
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Lorenzo Strani
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
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Hao Y, Lu Y, Li X. Study on robust model construction method of multi-batch fruit online sorting by near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 280:121478. [PMID: 35724593 DOI: 10.1016/j.saa.2022.121478] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 05/19/2022] [Accepted: 06/03/2022] [Indexed: 06/15/2023]
Abstract
In the online detection of fruit samples by near-infrared spectroscopy (NIRS), the natural change of sample states or the variations of instruments will often cause a large error in predicting different batches of samples. In this study, a total of 440 tomato samples were collected in four batches with each batch of 110 samples. The Spectral and soluble solids content (SSC) of single batch were collected every other day in batch order. The multivariate statistical process control (MSPC) method was adopted to establish a stability monitor model. The robustness regression (Rob-Reg) and partial least squares regression (PLSR) were used for mixed modeling of multiple batches of samples to eliminate the variability influence of sample and instrument states. The results show that MSPC can effectively monitor the consistency of the same batch samples measured at different times or different batches. The variation of sample attributes with spectral acquisition time has dramatically damaged the adaptation of PLSR models. The Rob-Reg method can predict the SSC of the different batches of samples at different collection times. Compared with the PLSR method, the correlation coefficient of prediction (Rp) was improved from 0.61 to 0.66, and the root mean square error of prediction (RMSEP) was decreased from 0.55 to 0.44 for Rob-Reg method. The RPD of 3.85 indicated that the model is excellent. The Robust modeling method can be well applied to fruit near-infrared online detection system.
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Affiliation(s)
- Yong Hao
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.
| | - Yuanhang Lu
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
| | - Xiyan Li
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
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Pérez-Beltrán CH, Jiménez-Carvelo AM, Torrente-López A, Navas NA, Cuadros-Rodríguez L. QbD/PAT—State of the Art of Multivariate Methodologies in Food and Food-Related Biotech Industries. FOOD ENGINEERING REVIEWS 2022. [DOI: 10.1007/s12393-022-09324-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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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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