Fourier Transform Near-infrared Reflectance Spectroscopy Research Articles

Estimation of Milk Fatty Acids Health Index as Milk Value Added Determinant using FT-NIRS

The current milk price system based on Total Plate Count (TPC) and Total Solids (TS) are less sensitive in determining milk quality. Milk fatty acids profiles reflected milk quality for human health. However, their determination using Gas Chromatography (GC) is impracticable to be included as a daily price decision determinant. The study aimed to find a model for milk value added based on milk fatty acids profiles that reflected milk quality for human health measured by pre-calibrated rapid Fourier Transform Near-Infrared Reflectance Spectroscopy (FT-NIRS) method. Two hundred fifty-six samples of milk were collected from 3 dairy farm areas. Samples were analyzed using a Milkotronic milk analyzer for fat, protein and lactose contents and Gas Chromatography (GC) for fatty acids. The data were inputted into the FT-NIRS spectrum for calibration. The regression model to calculate milk value-added that can be used as a bonus system was developed after classifying and weighting of Milk Fatty Acid Index (MFAI) determined based on expert judgment. The results showed that milk fatty acids profiles vary greatly. Eight parameters (CLA, C16:0, SFA, MUFA, LCFA, PUFA, C18:2 trans9, 12 and H/H) can be detected accurately using FT-NIRS and used in milk value-added calculation. Simpler equation was used Y = 16.38307 + 5.395582 CLA + 0.695062 PUFA - 0.0244 C18:2, trans 9, 12 with R2 = 0.950 and was validated insignificantly different as calculated from the 8 parameters. It is concluded that the milk processing industry can use milk fatty acids generated from FT-NIRS to add value to milk collected from smallholder farmers.

Comparison of a Low-cost Prototype Optical Sensor with Three Commercial Systems in Predicting Water and Nutrient Contents of Turfgrass

ABSTRACT Chemical soil and plant analyses are time-consuming, expensive, and labor-intensive. There are some optical systems used for this purpose; however, they are expensive and require expertise for their operation. The aim of this study was to develop a low-cost prototype optical sensor and compare it with three commercial systems (GreenSeeker NDVI (Normalized Difference Vegetation Index) meter, chromameter, Fourier transform-near infrared reflectance spectroscopy (FT-NIRS)) to determine water and nutrient concentrations including nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), copper (Cu), manganese (Mn), and zinc (Zn) of turfgrass (Lolium perenne L.). Study was conducted on an experimental field to which four different levels of nitrogen fertilizer were applied. Prediction models were developed using PLSR (Partial Least Square Regression) and their performances were evaluated using the criteria of SEP (Standard Error of Prediction) and R2. With the prototype optical sensor, NDVI gave the best result among 10 different vegetation indices for the prediction of water (SEP = 1.43%) and N (SEP = 0.28%). The best results (lowest SEP) were obtained with the FT-NIRS. However, there are some disadvantages of this system along with the other two instruments (chromameter and NDVI meter) of being expensive and requiring expertise in their operation. Low-cost and easy-to-use prototype optical sensor gave similar results with the NDVI meter and chromameter to predict water and nutrient concentrations except K, Cu, and Zn. An optical sensor similar to the prototype sensor could be developed commercially with low cost and used to estimate the water and nutrient concentration of turfgrass.

Effective Quantification of Tannin Content in Sorghum Grains Using Near-infrared Spectroscopy

This study was conducted to investigate the feasibility of determining tannin content in sorghum grains with near-infrared reflectance spectroscopy (NIRS). A total of 110 sorghum grain samples were collected. The data matrix of the pretreated NIRS was randomly divided into a calibration set (Nc=77 samples) and a prediction set (Np=33 samples). The analysis of tannin content was based on the colorimetric method of GBT 15686-2008. Diffuse reflectance spectra of 110 sorghum samples were generated on a Fourier-transform NIRS with a scanning range of 12800-4000 cm^-1 and resolution of 16 cm^-1 and 64 scans. Several spectra pretreatment methods were compared to for an optimum spectral pretreatment method. The optimal model was determined according to coefficient of determination for calibration (R²<SUB>CAL</sub>), root mean standard error of calibration (RMSE<SUB>CAL</sub>), coefficient of determination for cross-validation (R²<SUB>CV</sub>), root mean standard error of cross-validation (RMSE<SUB>CV</sub>) and the residual predictive deviation (RPD). The results showed that the tannin content of the sorghum grains ranged from 0.01% to 2.12% DM with the average of 0.58%, and first derivative was the optimal spectral pretreatment with the lowest RMSE<SUB>CV</sub> of 0.14. The absorption peaks of the optimal model mainly located at 9402-7492 cm^-1 and 5452-4244 cm^-1. The RPD of calibration, cross-validation and external validation were 6.22, 4.22 and 3.0, respectively. The findings suggest that the established model using NIRS is effective to quantify tannin content in sorghum grains rapidly.

Application of a Fourier transform—near infrared reflectance spectroscopy method for the rapid proximate analysis of the greenshell mussel ( Perna canaliculus ) and king (Chinook) salmon ( Oncorhynchus tshawytscha )

Greenshell™ mussel (GSM, Perna canaliculus) and king (Chinook) salmon (Oncorhynchus tshawytscha) are New Zealand's two major aquaculture species generating $380 million NZD in exports during the 2017–18 financial year. This study addresses the development and validation of a method based on Fourier transform—near infrared reflectance spectroscopy (FT-NIRs) to determine proximate composition for both species to aid breeding-, production- and consumer decisions. Rapid measurements of GSM (n = 176) were taken by FT-NIRs and analysed by traditional wet chemistry ‘reference methods’ to develop calibration models for proximate composition (protein, moisture, fat, ash and carbohydrate). The predictive models for moisture (r2 = 0.98, root mean square error of cross validation (RMSECV) = 0.314, residual prediction deviation (RPD = 6.47), protein (r2 = 0.91, RMSECV = 0.295, RPD = 3.01)) and carbohydrate (r2 = 0.87, RMSECV = 0.440, RPD = 2.78) in GSM performed well. Additional models based on 90 portions of salmon were developed to predict moisture (r2 = 0.98, RMSECV = 1.02, RPD = 7), protein (r2 = 0.96, RMSECV = 0.347, RPD = 5.08), fat (r2 = 0.99, RMSECV = 1.09, RPD = 5.98) and ash (r2 = 0.72, RMSECV = 0.05, RPD = 1.9). The predictive FT-NIRs and reference methods were tested for short-term and intermediate precision, which demonstrated that the repeatability of the predictive models was comparable to the reference methods. Proximate analysis of GSM and king salmon using FT-NIRs was quick (minutes for sample preparation and analysis rather than days) and all components were assessed simultaneously. This provides a low-cost short turn-around method suitable for industry and research applications.

Ultrahigh resolution total organic carbon analysis using Fourier Transform Near Infrarred Reflectance Spectroscopy (FT‐NIRS)

Fourier transform near infrared reflectance spectroscopy (FT‐NIRS) is a cheap, rapid, and nondestructive method for analyzing organic sediment components. Here, we examine the robustness of a within lake FT‐NIRS calibration using a data set of almost 400 core samples from Lake Suigetsu, Japan, as a means to rapidly reconstruct % total organic carbon (TOC). We evaluate the best spectra pretreatment, examine different statistical approaches, and provide recommendations for the optimum number of calibration samples required for accurate predictions. Results show that the most robust method is based on first‐order derivatives of all spectra modeled with partial least squares regression. We construct a TOC model training set using 247 samples and a validation test set using 135 samples (for test set R2 = 0.951, RMSE = 0.280) to determine TOC and illustrate the use of the model in an ultrahigh resolution (e.g., 1 mm/annual) study of a long sediment core from a climatically sensitive archive.

Rapid Determination of Water and Oil Content in Instant Noodles by Fourier Transform Near-Infrared Reflectance Spectroscopy

Rapid Determination of Water and Oil Content in Instant Noodles by Fourier Transform Near-Infrared Reflectance Spectroscopy

Fourier Transform Near-Infrared Spectroscopy to Predict the Gross Energy Content of Food Grade Legumes

The feasibility of Fourier transform near-infrared reflectance spectroscopy (FT-NIRS) for determining gross energy content in different food legumes has been investigated. Eighty food-grade legume samples were obtained from different retailers and local markets in Hungary and they included 42 common beans (Phaseolus vulgaris L.), 20 peas (Pisum sativum L.), 10 lentils (Lens culinaris L.), and 8 soya beans (Glycine max L.) both as full fat food and defatted. The samples were analyzed by an adiabatic bomb calorimeter and then scanned in a Bruker MPA FT-NIR Analyzer (800–2,500 nm). Two algorithms for spectral selection of calibration and validation samples, which represent variability encountered in the full population, were tested. Partial least squares regression were developed for the prediction of gross energy using four different spectral preprocessing methods (first and second derivative alone and combined with standard normal variation and multiplicative scatter correction). The results show that first derivative produced the most accurate results with very high coefficient of determinations in validation (<93 %) and with very low standard errors of validation (<0.025 kcal/g) as compared to the standard error of the reference method (0.204 kcal/g).

Screening for erucic acid and glucosinolate content in rapeseed-mustard seeds using near infrared reflectance spectroscopy

Using partial least square regression, calibration for non-destructive estimation of erucic acid and glucosinolate contents in seeds of rapeseed-mustard by Fourier transform near infrared reflectance spectroscopy (FT-NIRS) was developed. The calibration developed showed a very close relationship between the reference method for erucic acid (gas chromatography) and glucosinolate content (palladium complex formation) and NIR spectral data from 7502.1 to 5444.6cm. (-1) The coefficients of determinations were 97.16% and 98.34% for erucic acid and glucosinolate contents, respectively.

Potential Use of NIR Spectroscopy for the Estimation of Milled Rice Yield

Milled rice yield is one of the important indicators used to define the quality of milled rice. Near-infrared reflectance (NIR) spectroscopy was evaluated as a tool to estimate milled rice yield. Rough rice was dehusked and then milled to various milled rice yields. A total of 198 samples with milled rice yields of 85.72% to 96.91% were scanned over the NIR spectral wavelength ranging from 833 to 2500 nm with a Fourier transform near-infrared reflectance spectroscopy (FT-NIR) system. After finding the optimal spectral region (1638.8 to 2354.9 nm) according to the coefficients of determination, eleven mathematical pretreatments were performed on the selected spectra. The optimal calibration and prediction were obtained in the selected optimal spectral region using partial least square regression (PLS) on the spectra pretreated by a combination of first-derivation and multiplicative scattering correction. The best calibration model yielded a coefficient of determination (r2) of 0.994, a root mean square error of prediction (RMSEP) of 0.174%, and a bias of -0.021%, showing good predictability. Thus, the NIR spectroscopy technology has potential for predicting milled rice yield.

近赤外分光法の微分プロファイリングによる緑茶の特徴抽出およびランキングの予測

近赤外分光法の微分プロファイリングによる緑茶の特徴抽出およびランキングの予測

Prediction of Japanese Green Tea Ranking by Fourier Transform Near-Infrared Reflectance Spectroscopy

A rapid and easy determination method of green tea's quality was developed by using Fourier transform near-infrared (FT-NIR) reflectance spectroscopy and metabolomics techniques. The method is applied to an online measurement and an online prediction of green tea's quality. FT-NIR was employed to measure green tea metabolites' alteration affected by green tea varieties and manufacturing processes. A set of ranked green tea samples from a Japanese commercial tea contest was analyzed to create a reliable quality-prediction model. As multivariate analyses, principal component analysis (PCA) and partial least-squares projections to latent structures (PLS) were used. It was indicated that the wavenumber region from 5500 to 5200 cm(-1) had high correlation with the quality of the tea. In this study, a reliable quality-prediction model of green tea has been achieved.

PH—Postharvest Technology: Comparison of Fourier Transform and Dispersive Near-Infrared Reflectance Spectroscopy for Apple Quality Measurements

The large amount of publications about quality measurements of horticultural products with near-infrared (NIR) reflectance spectroscopy demonstrates its usefulness for measuring their internal composition in a non-destructive way. Up to now, dispersive instruments have been used exclusively to yield satisfactory calibration models. In this study, Fourier transform (FT) NIR spectroscopy has been compared to dispersive NIR spectroscopy. The instrument stability, the light penetration depth and the predictive capacity of some quality characteristics between both instruments were compared. The FT spectrophotometer achieved a higher signal-to-noise ratio. The light penetration depth in a Jonagold apple differed between the two instruments: the FT spectrophotometer established a lower penetration depth. Both instruments were able to measure the quality characteristics (soluble solids content, firmness and titratable acidity) with comparable accuracy. Based on the results, it was concluded that FT–NIR reflectance spectroscopy is an interesting alternative for standard dispersive instruments for the non-destructive quality evaluation of apples.