A single model to monitor multistep craft beer manufacturing using near infrared spectroscopy and chemometrics

França, Leandro de Moura; Grassi, Silvia; Pimentel, Maria Fernanda; Amigo, José Manuel

doi:10.1016/j.fbp.2020.12.011

Cited by 16 publications

(3 citation statements)

References 19 publications

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“…Concerning the alcoholic beverages, NIR analysis has been applied to assess the alcoholic strength of whiskies and vodkas [ 28 ] as well as other constituents of whiskies [ 28 , 29 ], rum and brandies [ 30 ], gin and vodka [ 31 ], and other distilled beverages [ 30 , 32 , 33 , 34 ] and to identify adulteration in distilled spirits [ 35 ]. Hanousek et al [ 36 ] performed calibration models for major volatile compounds and phenols of wine spirits based on least squares regression.…”

Section: Introductionmentioning

confidence: 99%

PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy

Anjos

Caldeira

Fernandes

et al. 2021

Sensors

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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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Section: Introductionmentioning

confidence: 99%

PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy

Anjos

Caldeira

Fernandes

et al. 2021

Sensors

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“…de Natarajan e Srinivasan (2010), que aplicam PCA para estudar a variabilidade de dados simulados da operação de uma plataforma de petróleo. Já França et al (2021), desenvolveram uma abordagem para monitorar todo o processo de produção de cerveja artesanal através da aplicação de PCA a dados de análise coletados em todas as etapas no processo e geração de um gráfico de controle multivariado.…”

Section: Referencial Teóricounclassified

Um Estudo De Correlação Entre Variáveis Da Planta De Processamento Primário De Uma Plataforma De Petróleo Com Teor De Óleos E Graxas Em Água Produzida E Descartada No Mar

Rigo¹,

Sobral²

2021

Anais Do IX Simpósio De Engenharia De Produção - SIMEP 2021

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Na legislação brasileira, o teor de óleo e graxas (TOG) é o principal parâmetro avaliado para determinar a qualidade da água produzida descartada por plataformas marítimas de produção de petróleo, tendo limites bem estabelecidos. Para sua obtenção é exigida análise gravimétrica da amostra de água, de maneira que existe um descolamento de semanas entre o descarte da água pela plataforma e o resultado da análise. Esse estudo busca compreender a relação entre as variáveis de processo da planta de processamento de petróleo em uma plataforma, que possui dentre seus propósitos o tratamento da água produzida, e o TOG na água produzida que é descartada. O objetivo é identificar quais as principais variáveis do processo são responsáveis pela qualidade da água descartada. Para isso, foi aplicada a metodologia de Análise de Componentes Principais (PCA) em um conjunto de dados de operação da planta coletados a uma taxa diária por um período de quatro meses. Ao todo, foram avaliadas 62 variáveis de processo. A análise dos dados foi realizada no software estatístico R.

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“…For this reason, different types of sensors that can provide timely information are becoming more and more used for on-line quality probing from raw materials to the semi-finished and final products. It has been widely demonstrated that NIR spectroscopy has a powerful potential in monitoring food production processes [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ], due to its ability to detect both chemical and physical changes in the samples. To cite a few applications: NIR has been used for process monitoring in the dairy industry, from the prediction of raw milk composition to milk coagulation in cheese production and yogurt fermentation [ 11 ]; the fermentation processes in the wine and brewery industries; and the powdered ingredients mixing stage in different food matrices [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics

Tanzilli

D’Alessandro

Tamelli

et al. 2023

Foods

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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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A single model to monitor multistep craft beer manufacturing using near infrared spectroscopy and chemometrics

Cited by 16 publications

References 19 publications

PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy

PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy

Um Estudo De Correlação Entre Variáveis Da Planta De Processamento Primário De Uma Plataforma De Petróleo Com Teor De Óleos E Graxas Em Água Produzida E Descartada No Mar

A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics

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