Temperature Compensation on Sugar Content Prediction of Molasses by Near-Infrared Spectroscopy (NIR)

Temperature Influence in Real-Time Monitoring of Fed-Batch Ethanol Fermentation by Mid-Infrared Spectroscopy

“…Therefore, although the model error (RMSECV) had the same order of magnitude for all compounds, only for the fructose the variation of the predicted concentration was greater than the model error. In order to overcome these thermal effects, calibration models were constructed using online data acquired during fermentations at different temperatures, employing the combined model technique. , …”

“…In order to overcome these thermal effects, calibration models were constructed using online data acquired during fermentations at different temperatures, employing the combined model technique. 17,18 3.2. Data Processing of Fed-Batch Fermentations.…”

“…Rathore et al 17 reported the interference of temperature when the NIR region was used to monitor the main compounds of ethanol fermentation from corn, observing that application of first derivative (1 st D) as a pretreatment reduced the baseline offset induced by temperature variation. In another study, Chapanya et al 18 used NIR spectra obtained at different temperatures to construct combined calibration models that were used to predict the concentrations of sucrose, glucose, and fructose at 25, 35, and 45 °C. The reference and predicted values (obtained using the combined model method) were not statistically different, showing the potential of this technique for the analysis of fermentable sugar in molasses.…”

Nondestructive monitoring storage quality of apples at different temperatures by near‐infrared transmittance spectroscopy

“…Considering the influence of temperature on the models, the mixed temperature correction (MTC) method was proposed to process the spectra. MTC method combined the spectral data of samples under different temperature conditions to establish a model, and the temperature information was involved in the model and analysis (Chapanya et al, 2018). In the processing of MTC, the accuracy of the prediction model depends on the number of representative samples of the calibration dataset, which needs to cover samples with a wide range of temperature changes.…”

“…The detection of wines using NIR spectroscopy especially at the spectra region of 970-1,400 nm has been proved to be affected by the temperature, and the optimal temperature for testing was found to be 30-35°C (Cozzolino et al, 2007). In order to compensate for the influence of temperature on modeling, the mixed temperature correction method and partial least squares regression (PLSR) models for prediction of sugar content of molasses have been developed by combining spectral data at different temperature conditions (Chapanya, Ritthiruangdej, Mueangmontri, Pattamasuwan, & Vanichsriratana, 2018). For apple fruit, long-term storage is required to meet the demands of annual supply.…”