NIR and PLS discriminant analysis for predicting the processability of malt during lautering

Krause, Daniel; Holtz, C.; Gastl, Martina; Hussein, Mostafa; Becker, Thomas

doi:10.1007/s00217-014-2389-3

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…Abnormal variations at the spectral level are also used to indicate potential outliers which would introduce error into the calibration model or that some erroneous error occurred during the scanning process which could be due to probe fouling or path length obstruction caused by aggregated cells. Principal component analysis is often used to classify sample scans in a qualitative or discriminant approach based on pattern recognition methods that are irrespective of reference data values . Grams AI was used to break each spectral scan down to its principle components in order to compare the relative weights (or scores) of each component from sample to sample.…”

Section: Resultsmentioning

confidence: 99%

“…Various calibrations have also been developed to measure parameters related to milled malt quality such as moisture, nitrogen, amino acids, fibre, starch and total β ‐glucans . In addition to monitoring quality parameters directly related to malt, research has shown that models can be developed to predict lautering performance based on NIR fingerprinting . Garden et al demonstrated that NIRS is a rapid and cost‐effective technique for measuring important hop constituents such as α ‐ and β ‐acids as well as the hop storage index.…”

Section: Introductionmentioning

confidence: 99%

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The application of near-infrared spectroscopy in beer fermentation for online monitoring of critical process parameters and their integration into a novel feedforward control strategy

2017

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Traditional methods used in the analysis of fermentation media suffer from a number of limitations. The search for more rapid and efficient methods has led to the development and application of near‐infrared spectroscopy. Near‐infrared spectroscopy has been applied successfully in a variety of industrial processes: agricultural, food, chemical and pharmaceutical, generally in the areas of raw material quality control but also including intermediate and finished product testing. The present research explores its potential for online fermentation monitoring of total cell count (TCC), specific gravity (SG), free amino nitrogen (FAN) and percentage alcohol by volume (% v v−1) in a 300 L pilot‐scale validation batch. Models that were generated from three calibration batches for each of these constituents exhibited overall favourable standard error of cross validation (SECV) and fit of predicted vs actual cross validated results (SECV, R2): SG (0.00072, 0.995), ethanol (0.17% v v−1, 0.990), FAN (16.5 mg L−1, 0.886) and TCC (1.24 × 106 cells mL−1, 0.640). The data that was most relevant to cell metabolism was determined to be sugar consumption rate, ethanol production rate, yield of ethanol and fermentation lag time. These ‘critical performance parameters’ were incorporated into a novel feed‐forward control strategy where yeast pitching rate was modified based on values of the critical performance parameters from the previous batch. Use of this feed‐forward strategy demonstrated how brewers can utilize near‐infrared monitoring for quality assurance through early detection of shifts in fermentation performance. Copyright © 2017 The Institute of Brewing & Distilling

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The application of near-infrared spectroscopy in beer fermentation for online monitoring of critical process parameters and their integration into a novel feedforward control strategy

2017

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“…The object of partial least squares is two data matrixes X(n × m x ) with Y(n × m y ). Partial least squares can solve the problems of collinearity [25] and insufficient samples [26] in traditional multivariate regression methods.…”

Section: Methodology 21 Partial Least Squares Methods (Pls)mentioning

confidence: 99%

Slow Time-Varying Batch Process Quality Prediction Based on Batch Augmentation Analysis

Zhao¹,

Huang²

2022

Sensors

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In this paper, focusing on the slow time-varying characteristics, a series of works have been conducted to implement an accurate quality prediction for batch processes. To deal with the time-varying characteristics along the batch direction, sliding windows can be constructed. Then, the start-up process is identified and the whole process is divided into two modes according to the steady-state identification. In the most important mode, the process data matrix, used to establish the regression model of the current batch, is expanded to involve the process data of previous batches, which is called batch augmentation. Thus, the process data of previous batches, which have an important influence on the quality of the current batch, will be identified and form a new batch augmentation matrix for modeling using the partial least squares (PLS) method. Moreover, considering the multiphase characteristic, batch augmentation analysis and modeling is conducted within each phase. Finally, the proposed method is applied to a typical batch process, the injection molding process. The quality prediction results are compared with those of the traditional quality prediction method based on PLS and the ridge regression method under the proposed batch augmentation analysis framework. The conclusion is obtained that the proposed method based on the batch augmentation analysis is superior.

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“…The authors correlated the close relationship of the red and white varieties of C. paradise in the HCA dendrogram with their chemical similarities [54]. In a previous contribution, Krause et al (2015) utilized NIR spectroscopy combined with partial least squares regression for the quality analysis of brewing malt. Their study showed the capability of NIR spectroscopy as a fingerprinting technique for the classification of the processability of malt [56].…”

Section: Nir (Near-infrared Spectroscopy)mentioning

confidence: 99%

Nondestructive Metabolomic Fingerprinting: FTIR, NIR and Raman Spectroscopy in Food Screening

Cebi,

Bekiroglu,

Erarslan

2023

Molecules

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In recent years, there has been renewed interest in the maintenance of food quality and food safety on the basis of metabolomic fingerprinting using vibrational spectroscopy combined with multivariate chemometrics. Nontargeted spectroscopy techniques such as FTIR, NIR and Raman can provide fingerprint information for metabolomic constituents in agricultural products, natural products and foods in a high-throughput, cost-effective and rapid way. In the current review, we tried to explain the capabilities of FTIR, NIR and Raman spectroscopy techniques combined with multivariate analysis for metabolic fingerprinting and profiling. Previous contributions highlighted the considerable potential of these analytical techniques for the detection and quantification of key constituents, such as aromatic amino acids, peptides, aromatic acids, carotenoids, alcohols, terpenoids and flavonoids in the food matrices. Additionally, promising results were obtained for the identification and characterization of different microorganism species such as fungus, bacterial strains and yeasts using these techniques combined with supervised and unsupervised pattern recognition techniques. In conclusion, this review summarized the cutting-edge applications of FTIR, NIR and Raman spectroscopy techniques equipped with multivariate statistics for food analysis and foodomics in the context of metabolomic fingerprinting and profiling.

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NIR and PLS discriminant analysis for predicting the processability of malt during lautering

Abstract: online trends. Both methods were compared to the expert opinion coinciding with 84 and 85 %, respectively.

Cited by 7 publications

References 30 publications

The application of near-infrared spectroscopy in beer fermentation for online monitoring of critical process parameters and their integration into a novel feedforward control strategy

The application of near-infrared spectroscopy in beer fermentation for online monitoring of critical process parameters and their integration into a novel feedforward control strategy

Slow Time-Varying Batch Process Quality Prediction Based on Batch Augmentation Analysis

Nondestructive Metabolomic Fingerprinting: FTIR, NIR and Raman Spectroscopy in Food Screening

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