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

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

10.21203/rs.3.rs-507588/v1 ◽

2021 ◽

Author(s):

Yang Chen ◽

Lingli Chen ◽

Meijin Guo ◽

Xu Li ◽

Jinsong Liu ◽

...

Keyword(s):

Real Time ◽

Fermentation Process ◽

Near Infrared ◽

Prediction Models ◽

Nir Spectroscopy ◽

Environmental Parameters ◽

Inhomogeneous System ◽

Least Squares Regression ◽

Experiment Platform ◽

On Line

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 VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon

Journal of Spectroscopy ◽

10.1155/2018/7450695 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Ting Wu ◽

Nan Zhong ◽

Ling Yang

Keyword(s):

Neural Network ◽

Cold Storage ◽

Storage Time ◽

Near Infrared ◽

High Efficiency ◽

Back Propagation ◽

Nir Spectroscopy ◽

Back Propagation Neural Network ◽

Least Squares Regression ◽

Test Sets

The cold storage time of salmon has a significant impact on its freshness, which is an important factor for consumers to evaluate the quality of salmon. The efficient, accurate, and convenient protocol is urgent to appraise the freshness for quality checking. In this paper, the ability of visible/near-infrared (VIS/NIR) spectroscopy was evaluated to predict the cold storage time of salmon meat and skin, which were stored at low-temperature box for 0~12 days. Meanwhile, a double-layer stacked denoising autoencoder neural network (SDAE-NN) algorithm was introduced to establish the prediction model without spectral pre-preprocessing. The results showed that, compared with the common methods such as partial least squares regression (PLSR) and back propagation neural network (BP-NN), the SDAE-NN method had a better performance due to its high efficiency in decreasing noise and optimizing the initial weights. The determination coefficient of test sets (R2test) and root mean square error of test sets (RMSEP) have been calculated based on SDAE-NN, for the salmon meat (skin), the R2test can reach 0.98 (0.92), and the RMSEP can reach 0.93 (1.75), respectively. It is highlighted that the algorithm is efficient and accurate and that the salmon meat would be more suitable for predicting freshness than the salmon skin. VIS/NIR spectroscopy combined with the SDAE-NN algorithm can be widely used to predict the freshness of various agricultural products.

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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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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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Ability of Vis-PIR spectroscopy to monitor changes in organic carbon of loamy soils at two depths

10.5194/egusphere-egu21-15021 ◽

2021 ◽

Author(s):

Hayfa Zayani ◽

Youssef Fouad ◽

Didier Michot ◽

Zeineb Kassouk ◽

Zohra Lili-Chabaane ◽

...

Keyword(s):

Organic Carbon ◽

Near Infrared ◽

Full Range ◽

Nir Spectroscopy ◽

Soil Samples ◽

Calibration Data ◽

Least Squares Regression ◽

Data Set ◽

Soil Carbon Stocks ◽

The Mean

Visible-Near Infrared (Vis-NIR) spectroscopy has proven its efficiency in predicting several soil properties such as soil organic carbon (SOC) content. In this preliminary study, we explored the ability of Vis-NIR to assess the temporal evolution of SOC content. Soil samples were collected in a watershed (ORE AgrHys), located in Brittany (Western France). Two sampling campaigns were carried out 5 years apart: in 2013, 198 soil samples were collected respectively at two depths (0-15 and 15-25 cm) over an area of 1200 ha including different land use and land cover; in 2018, 111 sampling points out of 198 of 2013 were selected and soil samples were collected from the same two depths. Whole samples were analyzed for their SOC content and were scanned for their reflectance spectrum. Spectral information was acquired from samples sieved at 2 mm fraction and oven dried at 40&#176;C, 24h prior to spectra acquisition, with a full range Vis-NIR spectroradiometer ASD Fieldspec&#174;3. Data set of 2013 was used to calibrate the SOC content prediction model by the mean of Partial Least Squares Regression (PLSR). Data set of 2018 was therefore used as test set. Our results showed that the variation &#8710;SOCobsobtained from observed values in 2013 and 2018 (&#8710;SOCobs = Observed SOC (2018) - Observed SOC (2013)) is ranging from 0.1 to 25.9 g/kg. Moreover, our results showed that the prediction performance of the calibrated model was improved by including 11 spectra of 2018 in the 2013 calibration data set (R&#178;= 0.87, RMSE = 5.1 g/kg and RPD = 1.92). Furthermore, the comparison of predicted and observed &#8710;SOC between 2018 and 2013 showed that 69% of the variations were of the same sign, either positive or negative. For the remaining 31%, the variations were of opposite signs but concerned mainly samples for which &#8710;SOCobs is less than 1,5 g/kg. These results reveal that Vis-NIR spectroscopy was potentially appropriate to detect variations of SOC content and are encouraging to further explore Vis-NIR spectroscopy to detect changes in soil carbon stocks.

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Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

Journal of Near Infrared Spectroscopy ◽

10.1177/09670335211057235 ◽

2022 ◽

pp. 096703352110572

Author(s):

Nicholas T Anderson ◽

Kerry B Walsh

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Short Wave ◽

Least Squares Regression ◽

Machine Learning Methods ◽

Transmission Geometry ◽

Modelling Techniques ◽

Test Sets ◽

Model Robustness

Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

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NIR Spectroscopy and Partial Least Squares Regression for the Determination of Phosphate Content and Viscosity Behaviour of Potato Starch

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.300 ◽

2001 ◽

Vol 9 (2) ◽

pp. 133-139 ◽

Cited By ~ 8

Author(s):

L.G. Thygesen ◽

S.B. Engelsen ◽

M.H. Madsen ◽

O.B. Sørensen

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Potato Starch ◽

Nir Spectroscopy ◽

Peak Time ◽

Phosphate Content ◽

Least Squares Regression ◽

Data Prediction

A set of 97 potato starch samples with a phosphate content corresponding to a phosphorus content between 0.029 and 0.11 g per 100 g dry matter was analysed using a Rapid Visco Analyzer (RVA) and near infrared (NIR) spectroscopy, (700–2498 nm). NIR-based prediction of phosphate content was possible with a root mean square error of cross-validation ( RMSECV) of 0.006% using PLSR (partial least squares regression). However, the NIR/PLSR model relied on weak spectral signals, and was highly sensitive to sample preparation. The best prediction of phosphate content from the RVA viscograms was a linear regression model based on the RVA variable Breakdown, which gave a RMSECV of 0.008%. NIR/PLSR prediction of the RVA variables Peak viscosity and Breakdown was successful, probably because they were highly related to phosphate content in the present data. Prediction of the other RVA variables from NIR/PLSR was mediocre (Through, Final Viscosity) or not possible (Setback, Peak time, Pasting temperature).

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