Presented study aimed to apply the near infrared spectroscopy approach in determining some related properties of soil mixed by biochars. Spectra data of soil samples were acquired using a self-developed NIRS instrument (PSD NIRS i16) in shortwave near infrared (SW-NIRS) range from 1000 to 1750 nm with optical gain 4x and co-added of 32 scans per acquisitions. Spectra data were corrected and enhanced using mean centering and peak normalization. Multivariate analysis namely principal component regression (PCR) were employed to develop NIRS based models followed with leverage validation. The results showed that mixed soil samples with biochar properties (K and N) can be determined simultaneously with maximum correlation coefficient are 0.86 and 0.77 for K and N respectively. Based on this obtained performance, it may conclude that SW-NIRS approach can be applied to determine related properties of mixed soil biochar samples rapidly.
Cocoa is one of main agricultural products cultivated in many tropical countries and processed onto several derivative products. To determine cocoa beans qualities, laboratory procedures based on solvent extractions were mainly used, however most of them are destructive and may cause environmental pollutions. The main purpose of this present study is to employ near infrared spectroscopy (NIRS) for rapid and non-destructive assessment of cocoa beans in form of fat content. Near infrared spectral data of cocoa bean samples were measured as diffuse reflectance in wavelength range from 1000 to 2500 nm. Reference fat contents were measured using standard laboratory methods. Prediction models were developed using principal component regression with raw and baseline corrected spectra data. The results showed that fat contents of cocoa beans can be predicted and determined with maximum correlation coefficient (r) of 0.89 and ratio prediction to deviation (RPD) index of 2.87 for raw spectra and r of 0.91, RPD of 3.18 for baseline spectra correction. It may conclude that NIRS was feasible to be applied as a rapid and non-destructive method for cocoa bean quality assessment.
Agro-industrial residues have been widely used as feedstuffs for animal production due to its abundant availability and relatively cheap. The aim of this study is to create a NIRS model in prediction of nutritive content’s cacao pod husk including dry matter, crude fat, and ash by means of partial least squares regression (PLSR) approach. This study utilized cacao pod husk acquisitioned by NIRS spectrum with the wavelength from 1000 to 2500 nm. Proximate analysis was applied to measure nutritive values of cacao pod husk. The result of the study indicated that NIRS technology by means of PLSR approach with the help of DT pretreatment can be used as sufficient model performance to predict nutritive values of cacao pod husk for crude fat, and ash with the value of 0.7 and 0.5 for r and R2 respectively. Meanwhile, the value for RMSEC was 0.3 and 0.7 and 1.5 for RPD. However, judging from prediction performance, current NIRS approach seems not to able to determine moisture content and dry matter due to lower R2 and r coefficient indexes. Thus, it required further spectra enhancement for a robust prediction. This study concluded that NIRS technology can be used as rapid and simultaneous model to predict nutritive values of feed samples.
Near Infrared Reflectance Spectroscopy (NIRS) is an alternative method that can be applied in the evaluation of feed ingredients. The purpose of this study was to evaluate the in vitro digestibility value of fermented cocoa pods using the NIRS method of feed analysis. This study used 18 samples of fermented cocoa shell waste, where the material used was fermented cocoa pod skin (KBK) then in vitro analysis was carried out. Samples were acquired spectrum by Thermo Integrative. Chemical analysis is intended to compare the data from chemical analysis and the NIRS method. The parameters analyzed were pH, dry matter digestibility (KCBK), and organic matter digestibility (KCBO). Spectrum data were processed using PLS with the pre-treatment methods of multiplicative scatter correction (MSC) and DeTrending (DT). Based on the analysis that has been carried out using the NIRS method with a prediction model that has been built on pH parameters, KCBK has a good predictive model with the DT pre-treatment method and KCBO has a good predictive model with the MSC method where (pH = LV: 8, r: 0 .79, R2: 0.62, RMSEC: 0.04 and RPD: 2.00; KCBK= LV: 8, r: 0.86, R2: 0.74, RMSEC: 1.30 and RPD: 2.02 ; KCBO= LV: 8, r: 0.88, R2: 0.78, RMSEC: 1.39 and RPD: 2.21). Rough predictions for pH were obtained with Non pre-treatment and MSC pre-treatment (pH and MSC = LV: 8, r: 0.74, R2: 0.55, RMSEC: 0.05, RPD 1.58) and rough predictions on KCBK were obtained with Non Preatment (KCBK=LV: 8, r: 0.83 R2:0.70, RMSEC: 1.30, and RPD 1.87).
This presented study aimed to study the near infrared spectroscopic features of cocoa pod husk samples used as raw materials for animal feedstuff. Spectral data of organic material samples contains chemical properties information that can be revealed through modelling, Thus, the study of this features is essential to assess and reveal buried respective information. Cocoa pod husk samples were obtained from several districts in Aceh Province, grinded and prepared as bulk samples. Diffuse reflectance spectral data for a total of 30 bulk cocoa pod husk samples were acquired and recorded in wavelength range from 1000 to 2500 nm. Spectral data were firstly projected onto principal component analysis to observe similarities among samples. Spectra correction, namely mean normalization was employed to enhance spectra features. The results showed that several chemical information related to cocoa properties can be revealed such as dry matter, crude protein, crude fibre, ether extract, nitrogen-free extract and ash content due to the second and third overtones pf combination bands O-H, C-O-H and N-H. Optimum wavelength for estimating cocoa pod husk attributes are in 1217, 1405-1474 nm, 1629 nm, 1906-1979 nm, and 2283 nm. Based on obtained study, it may conclude that several quality attributes of animal feed samples further can be determined by means of near infrared spectroscopy approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.