scholarly journals Rapid and Non-Destructive Monitoring of Moisture Content in Livestock Feed Using a Global Hyperspectral Model

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1299
Author(s):  
Daniel Dooyum Uyeh ◽  
Juntae Kim ◽  
Santosh Lohumi ◽  
Tusan Park ◽  
Byoung-Kwan Cho ◽  
...  
Keyword(s):  
Moisture Content ◽  
Visual Assessment ◽  
Global Model ◽  
Least Squares Regression ◽  
Feed Production ◽  
Commercial Feed ◽  
Storage Limit ◽  
Health Complications ◽  
Preprocessing Technique ◽  
Non Destructive

The dry matter (DM) content of feed is vital in cattle nutrition and is inversely correlated with moisture content. The established ranges of moisture content serve as a marker for factors such as safe storage limit and DM intake. Rapid changes in moisture content necessitate rapid measurements. A rapid and non-destructive global model for the measurement of moisture content in total mixed ration feed and feed materials was developed. To achieve this, we varied and measured the moisture content in the feed and feed materials using standard methods and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1000–2500 nm. The spectral data from the samples were extracted and preprocessed using seven techniques and were used to develop a global model using partial least squares regression (PLSR) analysis. The range preprocessing technique had the best prediction accuracy (R2 = 0.98) and standard error of prediction (2.59%). Furthermore, the visual assessment of distribution in moisture content made possible by the generated PLSR-based moisture content mapped images could facilitate precise formulation. These applications of HSI, when used in commercial feed production, could help prevent feed spoilage and resultant health complications as well as underperformance of the animals from improper DM intake.

scholarly journals CARS Algorithm-Based Detection of Wheat Moisture Content before Harvest

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 115
Author(s):  
Hong Ji ◽  
Wanzhang Wang ◽  
Dongfeng Chong ◽  
Boyang Zhang
Keyword(s):  
Moisture Content ◽  
Regression Model ◽  
Regression Models ◽  
Partial Least Squares Regression ◽  
Early Stage ◽  
Least Squares Regression ◽  
Calibration Sample ◽  
Non Destructive ◽  
Plsr Model ◽  
Variable Subset

To rapidly detect the wheat moisture content (WMC) without harm to the wheat and before harvest, this paper measured wheat and panicle moisture content (PMC) and the corresponding spectral reflectance of panicle before harvest at the Beijing Tongzhou experimental station of China Agricultural University. Firstly, we used correlation analysis to determine the optimal regression model of WMC and PMC. Secondly, we derived the spectral sensitive band of PMC before filtering the redundant variables competitive adaptive reweighted sampling (CARS) to select the variable subset with the least error. Finally, partial least squares regression (PLSR) was used to build and analyze the prediction model of PMC. At the early stage of wheat harvest, a high correlation existed between WMC and PMC. Among all regression models such as exponential, univariate linear, polynomial models, and the power function regression model, the logarithm regression model was the best. The determination coefficients of the modeling sample were: R2 = 0.9284, the significance F = 362.957, the determination coefficient of calibration sample R2v = 0.987, the root mean square error RMSEv = 3.859, and the relative error REv = 7.532. Within the range of 350–2500 nm, bands of 728–907 nm, 1407–1809 nm, and 1940–2459 nm had a correlation coefficient of PMC and wavelength reflectivity higher than 0.6. This paper used the CARS algorithm to optimize the variables and obtained the best variable subset, which included 30 wavelength variables. The PLSR model was established based on 30 variables optimized by the CARS algorithm. Compared with the all-sensitive band, which had 1103 variables, the PLSR model not only reduced the number of variables by 1073, but also had a higher accuracy in terms of prediction. The results showed that: RMSEC = 0.9301, R2c = 0.995, RMSEP = 2.676, R2p = 0.945, and RPD = 3.362, indicating that the CARS algorithm could effectively remove the variables of spectral redundant information. The CARS algorithm provided a new way of thinking for the non-destructive and rapid detection of WMC before harvest.

scholarly journals Moisture Content Measurement of Broadleaf Litters using Near-Infrared Spectroscopy Technique

2017 ◽  
Author(s):  
Ghiseok Kim ◽  
Suk-Ju Hong ◽  
Ah-Yeong Lee ◽  
Ye-Eun Lee ◽  
Sangjun Im
Keyword(s):  
Infrared Spectroscopy ◽  
Moisture Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Optimal Subset ◽  
Preprocessing Technique ◽  
Moisture Content Measurement ◽  
Suitable Technique ◽  
Plsr Model

Near-infrared spectroscopy (NIRS) was implemented to monitor the moisture content of broadleaf litters. Partial least-squares regression (PLSR) models, incorporating optimal wavelength selection techniques, have been proposed to better predict the litter moisture of forest floor. Three broadleaf litters were used to sample the reflection spectra corresponding the different degrees of litter moisture. Maximum normalization preprocessing technique was successfully applied to remove unwanted noise from the reflectance spectra of litters. Four variable selection methods were also employed to extract the optimal subset of measured spectra for establishing the best prediction model. The results showed that the PLSR model with the peak of beta coefficients method was the best predictor among all candidate models. The proposed NIRS procedure is thought to be a suitable technique for on-the-spot evaluation of litter moisture.

scholarly journals Moisture Measurement in Paper and Cardboard Packaging Waste Bales for Recycling

2021 ◽  
Vol 11 (10) ◽  
pp. 4586
Author(s):  
Ana Silveira ◽  
João Cardoso ◽  
Maria José Correia ◽  
Graça Martinho
Keyword(s):  
Quality Control ◽  
Dielectric Properties ◽  
Moisture Content ◽  
Reliable Method ◽  
Drying Method ◽  
Waste Streams ◽  
Oven Drying ◽  
Quality Issue ◽  
Commercial Device ◽  
Non Destructive

Moisture content is a quality issue raised by recycling plants in the acceptance of paper and cardboard coming from waste streams. The current way to measure this parameter is by the oven drying method, which is a slow and invasive process, costing time and resources for the recyclers to do this type of quality control. An alternative to such a measurement technique is the use of plate-form devices which indirectly measure the moisture content using the dielectric properties of water and paper. This study has tested this method and developed a representative equation for the use of devices with these properties in the Portuguese market. For that, 48 wastepaper and cardboard bales were tested with both the traditional (oven drying) method and a commercial device equipped with dielectric technology. An equation that fits the studied reality (R2 = 0.76) was achieved, and possible problems regarding the use of this device were tested. The results showed that this type of device could be used as a time- and cost-saving, non-destructive and reliable method in the quality control of wastepaper and cardboard bales.

Developing an electronic portable device based on dielectric power spectroscopy for non-destructive prediction of date moisture content

2016 ◽  
Vol 247 ◽  
pp. 289-297 ◽  
Author(s):  
Seyed Ahmad Mireei ◽  
Rahmatollah Bagheri ◽  
Morteza Sadeghi ◽  
Ali Shahraki
Keyword(s):  
Moisture Content ◽  
Portable Device ◽  
Non Destructive

Identification prediction moisture content of Thai coconut sugar (Cocos nucifera L.) using FT-NIR spectroscopy

Food Research ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 382-385
Author(s):  
D. Thumrongchote
Keyword(s):  
Moisture Content ◽  
Cocos Nucifera ◽  
Nir Spectroscopy ◽  
Grocery Stores ◽  
Absorbance Spectrum ◽  
Coconut Tree ◽  
Cocos Nucifera L ◽  
The Cost ◽  
Non Destructive ◽  
Low Glycemic Index

Coconut sugar is a local sugar from the blossoms of a coconut tree. It has been considered a healthy sugar due to its low glycemic index. There is an attempt to add other sugar to it to lower the cost. Thus, this research aimed to identify Thai coconut sugar and to establish models for predicting the moisture content of coconut sugar by using FT-NIR spectroscopy. Thai coconut sugar samples were purchased from local grocery stores in four provinces, online, and the community market. Their moisture contents were varied and equilibrated for 24 hrs prior to the measurements of moisture and FT-NIR spectra. The results showed that FT-NIR spectra of Thai coconut sugar differ from sucrose, glucose and fructose at the absorbance spectrum of 5379-5011 cm-1 . FT-NIR spectroscopy of 54 known moisture samples of Thai coconut sugar was used to obtain a model to predict moisture content. The predicted equation, using the PLS technique with the Spectrum Quant program, was found to give a standard error of prediction (SEP) 0.077% (less than 0.10%), indicating a non-destructive method of accurately and precisely predicting moisture levels in the coconut sugar. The results obtained suggested that FTNIR spectroscopy has the potential to be used as a tool to identify Thai coconut sugar accurately. It can rapidly predict the moisture content in the sample which will be useful in quality control standards.

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