Real-time and on-line parallel detection of key fermentation process parameters by near-infrared spectroscopy in different environments

Abstract The fermentation process is dynamically changing, and the metabolic status can be grasped through real-time monitoring of environmental parameters. In this study, a real-time and on-line monitoring experiment platform for substrates and products detection was developed based on non-contact type near-infrared (NIR) spectroscopy technology. The prediction models for monitoring the fermentation process of lactic acid, sophorolipids and sodium gluconate were established based on partial least-squares regression and internal cross-validation methods. Through fermentation verification, the accuracy and precision of the NIR model for the complex fermentation environments, different rheological properties (uniform system and multi-phase inhomogeneous system) and different parameter types (substrate, product and nutrients) have good applicability, and R2 is greater than 0.90, exhibiting a good linear relationship. The root mean square error shows that the model has high credibility. This research provides a basis for the application of NIR spectroscopy in complex fermentation systems.

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Application of near-infrared spectroscopy technology in the complex fermentation system to achieve high-efficiency production

Bioresources and Bioprocessing ◽

10.1186/s40643-021-00452-9 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Chen Yang ◽

Chen Lingli ◽

Guo Meijin ◽

Li Xu ◽

Liu jinsong ◽

...

Keyword(s):

Real Time ◽

Fermentation Process ◽

Near Infrared ◽

High Efficiency ◽

Prediction Models ◽

Nir Spectroscopy ◽

Environmental Parameters ◽

Inhomogeneous System ◽

Least Squares Regression ◽

Experiment Platform

AbstractThe fermentation process is dynamically changing, and the metabolic status can be grasped through real-time monitoring of environmental parameters. In this study, a real-time and on-line monitoring experiment platform for substrates and products detection was developed based on non-contact type near-infrared (NIR) spectroscopy technology. The prediction models for monitoring the fermentation process of lactic acid, sophorolipids (SLs) and sodium gluconate (SG) were established based on partial least-squares regression and internal cross-validation methods. Through fermentation verification, the accuracy and precision of the NIR model for the complex fermentation environments, different rheological properties (uniform system and multi-phase inhomogeneous system) and different parameter types (substrate, product and nutrients) have good applicability, and R2 was greater than 0.98, exhibiting a good linear relationship. The root mean square error of prediction shows that the model has high credibility. Through the control of appropriate glucose concentration in SG fermentation as well as glucose and oil concentrations SLs fermentation by NIR model, the titers of SG and SLs were increased to 11.8% and 26.8%, respectively. Although high cost of NIR spectrometer is a key issue for its wide application in an industrial scale. This work provides a basis for the application of NIR spectroscopy in complex fermentation systems.

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Estimation of Secondary Soil Properties by Fusion of Laboratory and On-Line Measured Vis–NIR Spectra

Remote Sensing ◽

10.3390/rs11232819 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2819 ◽

Cited By ~ 6

Author(s):

Muhammad Abdul Munnaf ◽

Said Nawar ◽

Abdul Mounem Mouazen

Keyword(s):

Soil Properties ◽

Real Time ◽

Measurement Accuracy ◽

Near Infrared ◽

Diffuse Reflectance Spectroscopy ◽

Spectral Response ◽

Nir Spectroscopy ◽

Accurate Estimation ◽

Line Measurement ◽

On Line

Visible and near infrared (vis–NIR) diffuse reflectance spectroscopy has made invaluable contributions to the accurate estimation of soil properties having direct and indirect spectral responses in NIR spectroscopy with measurements made in laboratory, in situ or using on-line (while the sensor is moving) platforms. Measurement accuracies vary with measurement type, for example, accuracy is higher for laboratory than on-line modes. On-line measurement accuracy deteriorates further for secondary (having indirect spectral response) soil properties. Therefore, the aim of this study is to improve on-line measurement accuracy of secondary properties by fusion of laboratory and on-line scanned spectra. Six arable fields were scanned using an on-line sensing platform coupled with a vis–NIR spectrophotometer (CompactSpec by Tec5 Technology for spectroscopy, Germany), with a spectral range of 305–1700 nm. A total of 138 soil samples were collected and used to develop five calibration models: (i) standard, using 100 laboratory scanned samples; (ii) hybrid-1, using 75 laboratory and 25 on-line samples; (iii) hybrid-2, using 50 laboratory and 50 on-line samples; (iv) hybrid-3, using 25 laboratory and 75 on-line samples, and (v) real-time using 100 on-line samples. Partial least squares regression (PLSR) models were developed for soil pH, available potassium (K), magnesium (Mg), calcium (Ca), and sodium (Na) and quality of models were validated using an independent prediction dataset (38 samples). Validation results showed that the standard models with laboratory scanned spectra provided poor to moderate accuracy for on-line prediction, and the hybrid-3 and real-time models provided the best prediction results, although hybrid-2 model with 50% on-line spectra provided equally good results for all properties except for pH and Na. These results suggest that either the real-time model with exclusively on-line spectra or the hybrid model with fusion up to 50% (except for pH and Na) and 75% on-line scanned spectra allows significant improvement of on-line prediction accuracy for secondary soil properties using vis–NIR spectroscopy.

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Near-Infrared Spectroscopy as an Analytical Process Technology for the On-Line Quantification of Water Precipitation Processes during Danhong Injection

International Journal of Analytical Chemistry ◽

10.1155/2015/313471 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Xuesong Liu ◽

Chunyan Wu ◽

Shu Geng ◽

Ye Jin ◽

Lianjun Luan ◽

...

Keyword(s):

Partial Least Squares Regression ◽

Near Infrared ◽

Nir Spectroscopy ◽

Least Squares Regression ◽

Process Technology ◽

Danhong Injection ◽

Fiber Optic Probes ◽

Quantitative Monitoring ◽

On Line ◽

Predicted Values

This paper used near-infrared (NIR) spectroscopy for the on-line quantitative monitoring of water precipitation during Danhong injection. For these NIR measurements, two fiber optic probes designed to transmit NIR radiation through a 2 mm flow cell were used to collect spectra in real-time. Partial least squares regression (PLSR) was developed as the preferred chemometrics quantitative analysis of the critical intermediate qualities: the danshensu (DSS, (R)-3, 4-dihydroxyphenyllactic acid), protocatechuic aldehyde (PA), rosmarinic acid (RA), and salvianolic acid B (SAB) concentrations. Optimized PLSR models were successfully built and used for on-line detecting of the concentrations of DSS, PA, RA, and SAB of water precipitation during Danhong injection. Besides, the information of DSS, PA, RA, and SAB concentrations would be instantly fed back to site technical personnel for control and adjustment timely. The verification experiments determined that the predicted values agreed with the actual homologic value.

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Applicability of near infrared spectroscopy for real-time soil detection during automatic dishwashing

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033518821835 ◽

2019 ◽

Vol 27 (3) ◽

pp. 183-190 ◽

Cited By ~ 1

Author(s):

Pola Heidrich ◽

Edith Lambert ◽

Arnd Kessler ◽

Michaela Gerstenlauer ◽

Heinz Heißler ◽

...

Keyword(s):

Real Time ◽

Near Infrared ◽

Cross Validation ◽

Prediction Models ◽

Egg Yolk ◽

Least Squares Regression ◽

Washing Process ◽

Automatic Adaptation ◽

Automatic Dishwashing ◽

Different Soils

The purpose of this study was to utilize NIR spectrometry to develop a novel method to detect and determine concentrations of different soils in dishwashing liquor during automatic dishwashing in real-time. If it is possible to differentiate between soils, this could be an opportunity to react specifically to them (e.g. by increasing the water temperature if fat components are not sufficiently emulsifying). The possibility of an automatic adaptation of the dishwashing process to different soils and soil levels could lead to a shorter, more environmentally friendly and cost-reducing process. In a first approach, an emulsion containing three soil types (oatmeal, egg-yolk and butterfat), water and detergent were used to develop NIR spectrometry prediction models. Transmittance spectra obtained with an Fourier transform near infrared (FT-NIR) spectrometer of testing standards of 76 automatic dishwashing cycles with seven samples per cycle were taken at various times during the main washing process for calibration (and validation) of the NIR spectrometry prediction models. The spectra were pretreated to develop NIR spectrometry prediction models for each type of soil using the partial least squares regression method with cross-validation. Overall, the coefficients of determination in cross-validation are R2 > 0.92 for all NIR spectrometry prediction models developed. The results of the prediction models developed show that NIR spectrometry technology is a promising method to predict different levels of predefined soils in dishwashing liquor. The NIR spectrometry models were applied to an automatic dishwashing process with soiled dishes instead of emulsions containing soils to test their applicability. The resulting dishwashing process could be tracked in real-time by the dissolved soil concentrations, observed in the dishwashing liquor.

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Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy

Frontiers in Plant Science ◽

10.3389/fpls.2021.720022 ◽

2021 ◽

Vol 12 ◽

Author(s):

Irsa Ejaz ◽

Siyang He ◽

Wei Li ◽

Naiyue Hu ◽

Chaochen Tang ◽

...

Keyword(s):

Near Infrared ◽

Prediction Models ◽

Low Cost ◽

Nir Spectroscopy ◽

Grain Morphology ◽

Whole Grains ◽

Least Squares Regression ◽

Sorghum Grains ◽

Sorghum Grain ◽

Non Destructive

Near-infrared spectroscopy (NIR) is a non-destructive, fast, and low-cost method to measure the grain quality of different cereals. However, the feasibility for determining the critical biochemicals, related to the classifications for food, feed, and fuel products are not adequately investigated. Fourier-transform (FT) NIR was applied in this study to determine the eight biochemicals in four types of sorghum samples: hulled grain flours, hull-less grain flours, whole grains, and grain flours. A total of 20 hybrids of sorghum grains were selected from the two locations in China. Followed by FT-NIR spectral and wet-chemically measured biochemical data, partial least squares regression (PLSR) was used to construct the prediction models. The results showed that sorghum grain morphology and sample format affected the prediction of biochemicals. Using NIR data of grain flours generally improved the prediction compared with the use of NIR data of whole grains. In addition, using the spectra of whole grains enabled comparable predictions, which are recommended when a non-destructive and rapid analysis is required. Compared with the hulled grain flours, hull-less grain flours allowed for improved predictions for tannin, cellulose, and hemicellulose using NIR data. This study aimed to provide a reference for the evaluation of sorghum grain biochemicals for food, feed, and fuel without destruction and complex chemical analysis.

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Spectral Range and Wood Surface Impacts on Equilibrium Moisture Content Estimation in Thermally Modified Beech Wood by FT-NIR Spectroscopy

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1208/1/012022 ◽

2021 ◽

Vol 1208 (1) ◽

pp. 012022

Author(s):

Nebojša Todorović

Keyword(s):

Moisture Content ◽

Equilibrium Moisture Content ◽

Cross Section ◽

Near Infrared ◽

Prediction Models ◽

Beech Wood ◽

Nir Spectroscopy ◽

Principal Component ◽

Least Squares Regression ◽

The Cross

Abstract Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares regression (PLS-R) were tested for the possibility of equilibrium moisture content (EMC) prediction in thermally modified beech wood (Fagus moesiaca C.). The samples were modified for 4h at temperatures of 170, 190 and 210 °C. After thermal modification, the samples were kept in a climatic chamber until EMC was reached. FT-NIR spectra (100 scans and 4 cm-1) were collected on the cross-section and radial surfaces at four points. PLS – R models were developed for four spectral regions: the first overtone, the second overtone, the third overtone and the combination band region. Applied thermal treatment caused a decrease of EMC by 42 % at 170 °C, by 53 % at 190 °C, and by 62 % at 210 °C. Principal component analysis (PCA) indicated that there is a difference both between treatments and between wood surfaces. The results of the spectra taken from the radial surface were, in all models, better than the spectra of the cross-section. Related to chemical changes, the first and second overtone region play an important role in the calibrations. The best prediction models for EMC of thermally modified beech wood were obtained from radial surface spectra in the first (Rp2=0.86, RPD=2.69) and second overtone region (Rp2=0.87, RPD=2.70). The obtain results could contribute to the development of predictive models in monitoring of EMC which could significantly improve the quality of industrial production of thermally modified wood.

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Water absorption thermodynamics in single wood pellets modelled by multivariate near-infrared spectroscopy

Holzforschung ◽

10.1515/hf.2008.071 ◽

2008 ◽

Vol 62 (4) ◽

Cited By ~ 7

Author(s):

Torbjörn A. Lestander

Keyword(s):

Water Content ◽

Near Infrared ◽

Calorific Value ◽

Least Squares Regression ◽

Wood Pellets ◽

Differential Heat ◽

On Line ◽

Explained Variation ◽

Test Sets ◽

Content Range

Abstract Samples of wood pellets were adjusted into six water content classes from 0% to 12%. The water content in single pellets varied between 0.1% and 14.2%. Three equations were constructed to estimate the differential heat of sorption (-ΔH) values from (1) fractal-geometry, (2) isosteric, and (3) calorimetric data. The ranges in calculated -ΔH of single pellets were (1) 133–1475, (2) 315–881, and (3) 195–1188 J g-1 water, respectively, across the studied moisture content range. Partial least squares regression was used to model near-infrared (NIR) spectra from single pellets and to predict -ΔH values and water content. The explained variation in test sets for the different models ranged from 97.1% to 99.9%. The shifts in peak absorbance for two water bands indicated that frequency in overtone vibration of O-H stretching and bending decreased, when water content was raised. Simulations of mixes between pellets of differential heat values showed that released heat was up to 0.03% of the gross calorific value of wood pellets. This heat may be a major contributor to initial temperature increases in pellet stacks during storage. The results indicate that on-line NIR based predictions of differential heat in wood pellets is possible to apply in the pellet industry.

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Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

10.5194/egusphere-egu21-3896 ◽

2021 ◽

Author(s):

Iva Hrelja ◽

Ivana Šestak ◽

Igor Bogunović

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Spectral Data ◽

Near Infrared ◽

Mean Squared Error ◽

Nir Spectroscopy ◽

Soil Samples ◽

Least Squares Regression ◽

Macro Scale ◽

Sentinel 2

Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.The study is performed in Mediterranean Croatia (44&#176; 05&#8217; N; 15&#176; 22&#8217; E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed Quercus ssp. and Juniperus ssp. forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22nd 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31st and September 5th 2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSEp) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSEp (Spectroscopy dataset RMSEp = 1.91; Sentinel-2 dataset RMSEp = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.Keywords: remote sensing, soil spectroscopy, wildfires, soil organic matterAcknowledgment: This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Per&#269;in is acknowledged for her cooperation during the laboratory work.

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Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

Journal of Spectroscopy ◽

10.1155/2018/8949741 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 8

Author(s):

Sylvio Barbon ◽

Ana Paula Ayub da Costa Barbon ◽

Rafael Gomes Mantovani ◽

Douglas Fernandes Barbin

Keyword(s):

Near Infrared ◽

Prediction Models ◽

Nir Spectroscopy ◽

Quality Parameters ◽

Chicken Meat ◽

Poultry Meat ◽

Spectral Information ◽

Support Vector ◽

Near Infrared Spectra ◽

Meat Samples

Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIEL∗a∗b∗, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.

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