Use Of Near Infrared Reflectance Spectroscopy Research Articles

In Vivo Insights: Near-Infrared Photon Sampling of Reflectance Spectra from Cranial and Extracranial Sites in Healthy Individuals and Patients with Essential Tremor

Near-infrared (NIR) spectroscopy is a powerful non-invasive technique for assessing the optical properties of human tissues, capturing spectral signatures that reflect their biochemical and structural characteristics. In this study, we investigated the use of NIR reflectance spectroscopy combined with chemometric analysis to distinguish between patients with Essential Tremor (ET) and healthy individuals. ET is a common movement disorder characterized by involuntary tremors, often making it difficult to clinically differentiate from other neurological conditions. We hypothesized that NIR spectroscopy could reveal unique optical fingerprints that differentiate ET patients from healthy controls, potentially providing an additional diagnostic tool for ET. We collected NIR reflectance spectra from both extracranial (biceps and triceps) and cranial (cerebral cortex and brainstem) sites in ET patients and healthy subjects. Using Partial Least Squares Discriminant Analysis (PLS-DA) and Partial Least Squares (PLS) regression models, we analyzed the optical properties of the tissues and identified significant wavelength peaks associated with spectral differences between the two groups. The chemometric analysis successfully classified subjects based on their spectral profiles, revealing distinct differences in optical properties between cranial and extracranial sites in ET patients compared to healthy controls. Our results suggest that NIR spectroscopy, combined with machine learning algorithms, offers a promising non-invasive method for the in vivo characterization and differentiation of tissues in ET patients.

Fast screening of total nutrient contents in strawberry leaves and spent growing media using NIRS.

In closed-loop soilless cultivation, the main nutrient sinks are nutrients retained either by the crop or in spent growing media. Measurement of nutrients in spent growing media and in the aboveground vegetative plant biomass at crop termination can be a tool for assessing and optimizing nutrient efficiency. The first aim of this study was to test the potential of near-infrared reflectance spectroscopy (NIRS) to forecast the various nutrient contents in strawberry leaves, which would then allow for assessment of crop nutrient status and total nutrient uptake by strawberry plants. The second aim was to test NIRS as a high throughput technique for assessing the N, K, Ca, Mg and organic matter (OM) content and the pH, EC and C:N and C:P ratios for a dataset of composts, plant fibers and spent growing media. The NIRS prediction model for fast screening of the total nutrient contents in spent growing media was compared with a single extraction method. A database with 369 dried and ground strawberry leaf samples with known contents of N, P, K, Ca, and Mg were scanned using NIRS. The database covered a range of leaf contents of 6-35 g N/kg dry matter (DM), 0.7-6.3 g P/kg DM and 2-29 g K/kg DM. A dataset of 458 samples of different types of materials used in growing media was validated with a dataset of 109 samples. Validation for the strawberry leaves indicated potential for this application, with R2 values of 0.90 or higher for N, K and Ca, and R2 values higher than 0.85 for P and Mg. Validation for the dataset of composts, plant fibers and spent growing media also indicated the potential for this application, with R2 values of 0.90 or higher for organic matter, and with R2 values of 0.85 or higher for total Ca, pH and C:N. A first test indicated potential for the calibration based on fresh samples of compost, plant fiber as well as spent growing media or dried (not ground) samples. Use of NIRS on fresh samples would eliminate the need for drying and grinding the samples and would reduce screening time. The ammonium acetate extraction is a reliable alternative to NIRS for fast screening of the total P, K, Ca, and Mg contents in composts, plant fibers and spent growing media.

Use of near-infrared reflectance spectroscopy on feces to estimate digestibility and dry matter intake of dietary nutritional characteristics under grazing conditions in Colombian creole steers.

Digestibility and intake are parameters difficult and expensive to estimate under grazing conditions; therefore, the aim of this study was to develop near-infrared reflectance spectroscopy (NIRS) calibrations applied to feces (F-NIRS) and evaluate their accuracy to predict dry matter digestibility (DMD) and dry matter intake (DMI) of Colombian creole cattle. Five digestibility trials using creole steers were conducted; indigestible neutral detergent fiber (iNDF) was used as internal marker and Cr2O3 and TiO2 as external markers. A total of 249 forage and 396 fecal samples from individual animals were collected, dried, and grinded for conventional chemical analysis. For spectral analysis, fecal samples were pooled across collection periods (77 samples). Chemometric analysis was performed using WinISI V4.10 software applying the modified partial least squares method. Cross-validation was performed to avoid overfitting the models. The goodness-of-fit statistics considered were the coefficient of determination in cross-validation and prediction sets (R2cv and r2, respectively) and the ratio performance deviation (RPD). Fecal NIRS calibrations developed for forage and supplement DMD showed a satisfactory fit (R2cv =0.87 and RPD=2.77 and R2cv=0.92 and RPD=3.50, respectively). The accuracy of fecal output equations using chromium (Cr) and titanium (Ti) was similar in terms of R2cv (0.92) and RPD (3.63 vs. 3.57). Total DMI equations using Ti performed better compared to Cr (R2cv = 0.82 vs. 0.78; RPD=2.41 vs. 2.17, respectively). The F-NIRS models were validated using a completely independent set of fecal samples showing a moderate fit (r2>0.8 and RPD>2.0). This study showed that F-NIRS is a feasible tool to predict DMD and DMI of creole steers under grazing conditions. However, previous to socialization, this requires an improvement in accuracy of the calibrated equations related to grazing animals in different production contexts.

Continuous near-infrared reflectance spectroscopy monitoring to guide distal perfusion can minimize limb ischemia surgery for patients requiring femoral venoarterial extracorporeal life support

BackgroundPatients requiring femoral venoarterial (VA) extracorporeal life support (ECLS) are at risk of distal lower limb hypoperfusion and ischemia of the cannulated leg. In the present study, we evaluated the effect of using continuous noninvasive lower limb oximetry with near-infrared reflectance spectroscopy (NIRS) to detect tissue hypoxia and guide distal perfusion catheter (DPC) placement on the rates of leg ischemia requiring surgical intervention. MethodsWe performed a retrospective analysis of patients who had undergone femoral VA-ECLS at our institution from 2010 to 2014 (pre-NIRS era) and 2017 to 2021 (NIRS era). Patients who had undergone cannulation during the 2015 to 2016 transition era were excluded. The baseline characteristics, short-term outcomes, and ischemic complications requiring surgical intervention (eg, fasciotomy, thrombectomy, amputation, exploration) were compared across the two cohorts. ResultsOf the 490 patients included in the present study, 141 (28.8%) and 349 (71.2%) had undergone cannulation before and after the routine use of NIRS to direct DPC placement, respectively. The patients in the NIRS cohort had had a greater incidence of hyperlipidemia (53.7% vs 41.1%; P = .015) and hypertension (71.4% vs 60%; P = .020) at baseline, although they were less likely to have been supported with an intra-aortic balloon pump before ECLS cannulation (26.9% vs 37.6%; P = .026). These patients were also more likely to have experienced cardiac arrest (22.9% vs 7.8%; P ≤ .001) and a pulmonary cause (5.2% vs 0.7%; P = .04) as an indication for ECLS, with ECLS initiated less often for acute myocardial infarction (15.8% vs 34%; P ≤ .001). The patients in the NIRS cohort had had a smaller arterial cannula size (P ≤ .001) and a longer duration of ECLS support (5 vs 3.25 days; P ≤ .001) but significantly lower rates of surgical intervention for limb ischemia (2.6% vs 8.5%; P = .007) despite comparable rates of DPC placement (49.1% vs 44.7%; P = .427), with only two patients (1.1%) not identified by NIRS ultimately requiring surgical intervention. ConclusionsThe use of a smaller arterial cannula (≤15F) and continuous NIRS monitoring to guide selective insertion of DPCs could be a valid and effective strategy associated with a reduced incidence of ischemic events requiring surgical intervention.

Faecal near-infrared reflectance spectroscopy profiling for the prediction of dietary nutritional characteristics for equines

Faecal near infrared reflectance spectroscopy (NIRS) profiling involves the use of NIRS for the prediction of nutrient components of animal diets from their faecal samples. This study evaluated the potential use of faecal NIRS profiling for the prediction of minerals, fibre fractions, and organic matter (OM) in horses’ diet. Two feeding trials were conducted consisting of 29 horses housed in individual stables and fed unique diets (D) to obtain diet reference data and faecal (F) NIR spectra (D:F) pairs. The D:F pairs were used for the development of faecal NIRS predictive equations. The trials ran for a total of five weeks (7 days adaptation and 4 weeks of measurement). Diets were adjusted weekly by changing the forage and concentrate supplied to include variation in the diet. Faecal and diet samples were pooled per week per horse resulting in 116 D:F pairs (29 horses x 4 weeks). Samples from weeks 1–3 (n = 87) were used to develop calibration and week 4 samples were used as an independent validation set (n = 29). Modified partial least square regression was used for calibration development. Results obtained showed that faecal NIRS prediction of dietary OM was excellent [coefficient of determination of validation (R2val) = 0.92 and ratio performance deviation (RPD) = 3.54], while that of neutral detergent fibre (aNDF) was very good (R2val and RPD: 0.83 and 2.12). The validation statistics (R2val: 0.77 and RPD: 1.56) showed the prediction model for acid detergent fibre (ADF) was acceptable but not as precise as those of OM and aNDF. Prediction of acid detergent lignin (ADL) was the poorest (R2val = 0.41and RPD = 1.05). For the mineral components, prediction of copper (Cu) was very good (R2val = 0.81, RPD= 2.11) and better than those of Phosphorus (P), Sulphur (S) and Manganese (Mn) which all had R2val of 0.60–0.67 and RPD of 1.60–1.64. Overall, this study showed that faecal NIRS profiling can be used to successfully predict OM, aNDF and Cu concentration in horses’ diet. Although, the predictions of other fibre fractions were less satisfactory, faecal NIRS can still be used for monitoring purposes and improved for future application to independent samples.

The use of near-infrared reflectance spectroscopy (NIRS) to predict dairy fibre feeds in vitro digestibility

Information on dairy fibre feed digestibility is important in ration formulation to better predict dairy cattle performance. However, its measurement takes time. Near-infrared reflectance spectroscopy (NIRS) is a rapid, precise, and cost-effective method to predict nutrient value, such as chemical content and digestibility of feedstuffs. This study aims to develop a database for an in vitro digestibility prediction model using NIRS, including dry matter digestibility (DMD), neutral and acid detergent fibre digestibility (NDFD and ADFD), and hemicellulose digestibility (HSD). Eighty dietary fibre feeds consisting of Napier grass, natural grass, rice straw, corn stover, and corn-husk were collected from four dairy farming areas in West Java (Cibungbulang District of Bogor Regency, Parung Kuda District of Sukabumi Regency, Pangalengan District of Bandung Regency, and Lembang District of West Bandung Regency). The spectrum for each sample was collected thrice using NIRSflex 500, which was automatically separated by 2/3 for calibration and 1/3 for validation. External validation was conducted by measuring 20 independent samples. Calibration and validation models were carried out by NIRCal V5.6 using the partial least squares (PLS) regression. The results showed that all parameters produce r2 > 0.5 except for ADFD. Relative prediction deviation (RPD) > 1.5 was only found in hemicellulose digestibility prediction. RPL (SEP/SEL) <1.0 were found in DMD and hemicellulose digestibility. It is concluded that hemicellulose digestibility can be predicted using NIRS accurately while other parameters need improvement.

Application of Near Infrared Reflectance (NIR) spectroscopy to predict the moisture, protein, and fat content of beef for gourmet hamburger preparation

Near Infrared Reflectance (NIR) spectroscopy provides a rapid, easy to use, and non-destructive alternative to the methods traditionally employed in the determination of the chemical composition of food stuff, since it does not require reagents, time consuming procedures based on manual labor, and can determine multiple parameters simultaneously. Therefore, the use of NIR spectroscopy was evaluated in the quantification of the moisture, fat, and crude protein content of raw bovine meat, in order to support the development for industrial use of an Azorean Protected Geographical Indication (PGI) beef hamburgers, with a target fat content of 20 g/100 g. Sixty-six meat samples were studied, including the carcass cut of neck, shin, brisket, chuck and blade, flanks (thin flank and forequarter flank), plate ribs, ribs and flanks, mixture of unspecified cuts (minced meat), and commercial hamburgers. The samples were chemically analyzed according to standard methods, for moisture (drying method), total fat (solvent extract), and crude protein content (micro-Kjeldahl method). It was observed that the samples had a high fat content heterogeneity. The spectra of all samples were collected (400–2500 nm) and a calibration was performed using partial least squares regression (PLS) and cross validation. Several spectral pre-treatment techniques were applied, including the standard normal variate (SNV). The best calibration for the chemical properties under evaluation showed coefficient of determination (R2) and standard error of cross validation (SECV) values of 0.93 and 1.25%, respectively, for fat content, 0.89 and 0.99% for crude protein, and 0.72 and 2.18% for moisture. It was concluded that the use of NIR spectroscopy provides a good estimate of the fat and protein content of raw bovine meat.

Near‐infrared reflectance spectroscopy for forage nutritive value analysis in sub‐Saharan African countries

AbstractLimited supply of quality feed is the most common problem limiting livestock productivity in sub‐Saharan Africa (SSA). Routine feed evaluation is indispensable for formulating balanced rations, feed characterization, safety, and minimizing the environmental impact of livestock. Traditional wet chemistry has not met this demand in SSA because it is time consuming, expensive, reliant on imported reagents and equipment that requires regular maintenance. Near infrared reflectance spectroscopy (NIRS) is a rapid and accurate alternative. The NIRS can help meet the need to characterize locally available forages and feeds on the continent, thus allowing formulation of optimally balanced and safe rations, facilitating establishment of nutritive value‐based pricing, and improving feed marketing and environmental stewardship. Though several NIRS systems have been purchased in many SSA countries, few are currently used. Reasons include high upfront costs, lack of requisite technical capacity, lack of access to comprehensive wet chemistry‐based databases to develop and validate robust and accurate predictive equations, lack of access to or relevance of existing validated equations, and limited awareness about the value of NIRS. Recently developed portable devices can dramatically reduce cost, while providing flexibility and comparable accuracy to benchtop systems. Formation of NIRS consortia and communities of practice including public–private partnerships that link equipment, pool resources, and provide periodic training and troubleshooting, can address many of these problems. This paper elaborates the potential for using NIRS to improve feed analysis in SSA countries, the reasons for the low use of existing systems, and strategies to improve the adoption and use of NIRS.

Ethanol-Soluble Carbohydrates of Cool-Season Grasses: Prediction of Concentration by Near-Infrared Reflectance Spectroscopy (NIRS) and Evaluation of Effects of Cultivar and Management

Ethanol-soluble carbohydrates (ESCs) of cool-season grasses include mono- and disaccharides and sometimes short-chain fructans, which may exacerbate the risk of pasture-associated laminitis. A calibration for prediction of ESC concentrations by near-infrared reflectance spectroscopy (NIRS) was developed from 323 samples of four cool-season grass species (orchardgrass, Kentucky bluegrass, tall fescue, and perennial ryegrass) across 10 cultivars collected in central Kentucky in the morning and afternoon over two growing seasons. The calibration, which had accuracy above 95%, was used to predict ESC concentrations of 1,532 samples from the second growing season. ESC concentrations increased in the afternoon compared to the morning across all cultivars. In the majority of samples, ESC concentrations were not affected by nitrogen application to plots. Use of NIRS has the potential to evaluate management and cultivar effects on ESC concentrations in cool-season grass pastures.

Near Infrared Reflectance Spectroscopy Coupled to Chemometrics as a Cost-Effective, Rapid, and Non-Destructive Tool for Fish Fraud Control: Monitoring Source, Condition, and Nutritional Value of Five Common Whitefish Species.

Fish fraud is a problematic issue for the industry. For it to be properly addressed will require the use of accurate, rapid, and cost-effective tools. In this work, near infrared reflectance spectroscopy (NIRS) was used to predict nutritional values (protein, lipids, and moisture) as well as to discriminate between sources (farmed vs. wild fish) and conditions (fresh or defrosted fish). Samples of five whitefish species-Alaskan pollock (Gadus chalcogrammu), Atlantic cod (G. morhua), European plaice (Pleuronectes platessa), common sole (Solea solea), and turbot (Psetta maxima)-including farmed, wild, fresh, and frozen ones, were scanned by a low-cost handheld near infrared reflectance spectrometer with a spectral range between 900 and 1700 nm. Several machine learning algorithms were explored for both regression and classification tasks, achieving precisions and coefficients of determination higher than 88% and 0.78, respectively. Principal component analysis (PCA) was used to cluster samples according to classes where good linear discriminations were denoted. Loadings from PCA revealed bands at 1150, 1200, and 1400 nm as the most discriminative spectral regions regarding classification of both source and condition, suggesting the absorbance of OH, CH, CH2, and CH3 groups as the most important ones. This study shows the use of NIRS and both linear and non-linear learners as a suitable strategy to address fish fraud and fish QC.

Log‐ratio transformation is the key to determining soil organic carbon fractions with near‐infrared spectroscopy

SummaryInformation about soil organic carbon fractions is important in understanding the vulnerability of soil carbon to climate change and land management. Soil organic carbon can be divided into fractions that are labile and others that are more stable. All existing methods to fractionate soil organic carbon are time consuming and complex. Near‐infrared reflectance spectroscopy (NIRS) is a rapid analytical technique. In this study we evaluated and optimized the use of NIRS to predict soil organic carbon fractions with the constraint that the carbon fractions (labile and stabilized) should add up to 100% (total organic carbon content). We used samples from two datasets from agricultural and forest sites in Germany (dataset A) and Europe (dataset B). Samples were fractionated by two different methods (density and physical‐chemical fractionation) as reference methods. Soil samples were scanned in the NIR range and calibration models were developed using partial least squares regression. The key to improving model performance was the log‐ratio transformation proposed by Aitchison for compositional data that enabled us to model the fractions as dependent variables. Traditional methods for the optimization of NIRS models, such as selection of wavelength range and pretreatment of spectra, showed no effective reduction in error. With the constraint that both fractions add to 100% (log‐ratio transformation), the ratio of performance to deviation (RPD) increased from 1.6 to 2.8 for the labile C fraction and from 1.5 to 3.2 for the stabilized C fraction. Root mean square error of cross‐validation of the labile fraction was 4.3 g C kg−1for dataset A and 2.7 g C kg−1for dataset B, which corresponds to anR2of 0.88–0.80 and RPD of 2.9–2.2, respectively. The models performed equally well for different soil textures and land‐use types. With log‐ratio transformation, more precise calibrations of NIRS for C fractions could be obtained.HighlightsNear‐infrared spectroscopy can predict soil carbon fractions at different spatial extentsLog‐ratio transformation satisfies the constraint that fractions should add up to 100%Log‐ratio transformation is the key to improving calibrations for soil C fractions.The NIR models were tested with two fractionation schemes comprising different land uses and soil texture

Nutrient retention and release in coarse woody debris of three important central European tree species and the use of NIRS to determine deadwood chemical properties

BackgroundCoarse woody debris (CWD) is very important for forest ecosystems, particularly for biodiversity and carbon storage. Its relevance as a possible reservoir and source of nutrients is less clear, especially in central Europe.MethodsBased on a chronosequence of known ages of logs, we analyzed the nutrients stored in CWD of Fagus sylvatica, Picea abies, and Pinus sylvestris at different sites in Germany. To quantify nutrient concentrations, we assessed the use of Near Infrared Reflectance Spectroscopy (NIRS) to determine the chemical properties of CWD.ResultsNIRS models were suitable to predict concentrations of C, N, P, lignin and extractives. Concentrations of most nutrients increased with mass loss, with the exception of potassium, which decreased for beech and pine and remained relatively constant for spruce. The highest nutrient concentrations (N, P, S, Ca and Mn, except Mg and K) were generally observed in highly decomposed spruce logs. The net effect of decreasing CWD mass and increasing nutrient concentrations was either a decreasing (N, P and K in beech; P, Mg, K and Mn in pine), constant (S, Ca and Mg in beech; N, S and Ca in pine) or increasing amount of nutrients (N, P, S and Ca in spruce; Mn in beech) in the logs over the course of decomposition. The C/N ratio decreased for all tree species, most markedly for spruce from ca. 1000 at the beginning of the decomposition process to 180 at 36 years. The N/P ratio converged to a value of about 30 for all three species. Lignin concentrations increased for spruce and beech and remained constant for pine.ConclusionsOur results indicate that most nutrients remain in CWD for long periods. Nutrients may be used and cycled by microorganisms within CWD, but with the exception of P (in beech), Mg (in pine) and K (in beech and pine), there appears to be little net nutrient export until two thirds of the mass is lost. Instead, N, P, S and Ca were accumulated in spruce logs, indicating that CWD became a net sink rather than a net source of some nutrients for several decades.

Usefulness of Near-infrared Reflectance Spectroscopy to Assess Lower-limb Perfusion on Femoral Veno-arterial Extracorporeal Membrane Oxygenation

Usefulness of Near-infrared Reflectance Spectroscopy to Assess Lower-limb Perfusion on Femoral Veno-arterial Extracorporeal Membrane Oxygenation

Use of near-infrared reflectance spectroscopy for the rapid determination of the digestible energy and metabolizable energy content of corn fed to growing pigs.

BackgroundThe ability of near-infrared reflectance spectroscopy (NIRS) to determine the digestible energy (DE) and metabolizable energy (ME) content of corn fed to growing pigs was tested. One hundred and seventeen corn samples, comprising different planting regions and varieties were collected from all over China in a three-year period. The samples were randomly split into a calibration set (n = 88) and a validation set (n = 29). The actual and calculated DE and ME content of the corn samples was determined by digestion-metabolism experiments and the prediction equations of Noblet and Perez (J Anim Sci. 71:3389–98,1993). The samples were then subjected to NIRS scanning and calibrations were performed by the modified partial least square (MPLS) regression method based on 77 different spectral pre-treatments. The NIRS equations based on the actually determined and calculated DE and ME were built separately and then validated using validation samples.ResultsThe NIRS equations obtained from actually determined DE, the coefficient of determination for calibration (RSQcal), cross-validation (R2CV), and validation (RSQv) were 0.89, 0.87 and 0.86, and these values for determined ME were 0.87, 0.86 and 0.86. For the NIRS equations built from calculated DE, the RSQcal, R2CV, and RSQv values were 0.88, 0.85 and 0.84, and these values for calculated ME were 0.86, 0.84 and 0.82. Except for the equation based on calculated ME (RPDv = 2.38, < 2.50), the other three equations built from actually determined energy and calculated DE produced good prediction performance (RPDv ranging from 2.53 to 2.69, > 2.50) when applied to validation samples.ConclusionThese results indicate that NIRS can be used as a quantitative method for the rapid determination of the available energy in corn fed to growing pigs, and the NIRS equations based on the actually determined energy produced better predictive performance than those built from calculated energy values.

Use of near-infrared reflectance spectroscopy to quantify diet mixing in a generalist marine herbivore

The diet of individual animals in the field is governed by behavioural preference as well as factors intrinsic and extrinsic to the organism that may constrain their ability to obtain preferred foods. Accurate measurements of individual diets are essential to understanding how consumers can impact communities, but can be difficult to obtain. We aim to determine whether near-infrared reflectance spectroscopy (NIRS) can be used to quantify the degree of diet mixing in the diet of a generalist herbivore, the marine gastropod Lunella torquatus. NIRS successfully classified five algal diets and could quantify the proportions of these species in two-species combinations. We then assessed whether NIRS could predict dietary composition post-digestion by contrasting faecal material from single-species and mixed diets. Partial least squares methods were used to develop prediction models that effectively discriminated among species and, for most species, effectively predicted the proportion of a given species in all possible two-species mixtures. NIRS thus has the potential to provide a non-invasive method for assessing the realised diets of free-living herbivores.

Pasting properties by near-infrared reflectance analysis of whole grain paddy rice samples

A rapid predictive method based on near-infrared reflectance spectroscopy (NIRS) of paddy rice was developed to measure the pasting properties of rice. The paddy rice samples were scanned by a near-infrared reflectance spectrometer in the wavelength region of 1400–2400 nm and preprocessed by mathematical pretreatments prior to pasting properties analysis using a rapid visco-analyzer (RVA). The results indicated that the developed models of setback (SB), peak viscosity (PV), breakdown (BD) and consistency (CS) provided good prediction results with relatively high correlation coefficients (0.81–0.96). In addition, the validity of the calibration models was statistically tested. Standard error of prediction (SEP) and bias were small enough without any significance at 95% confidence interval. Nonetheless, this study proved that the use of NIRS for predicting pasting properties was feasible in paddy rice and could be applied in commercial trade and research.

예측알고리즘 적용을 위한 데이터세트 구성이 근적외선 분광광도계를 이용한 옥수수 품질평가에 미치는 영향

The objectives were to assess the use of near-infrared reflectance spectroscopy (NIRS) as a tool for estimating nutrient compositions of corn kernel, and to apply an NIRS-based indium gallium arsenide array detector to the system for collecting spectra and analyzing calibration equations using equipments designed for field application. Partial Least Squares Regression (PLSR) was employed to develop calibration equations based on representative data sets. The kennard-stone algorithm was applied to induce a calibration set and a validation set. As a result, the method for structuring a calibration set significantly affected prediction accuracy. The prediction of chemical composition of corn kernel resulted in the following (kennard-stone algorithm: relative) moisture (<TEX>$R^2=0.82$</TEX>, RMSEP=0.183), crude protein (<TEX>$R^2=0.80$</TEX>, RMSEP=0.142), crude fat (<TEX>$R^2=0.84$</TEX>, RMSEP=0.098), crude fiber (<TEX>$R^2=0.74$</TEX>, RMSEP=0.098), and crude ash (<TEX>$R^2=0.81$</TEX>, RMSEP=0.048). Result of this experiment showed the potential of NIRS to predict the chemical composition of corn kernel.

Application of near-infrared reflectance spectroscopy for predicting carotenoid content in summer squash fruit

The potential of near-infrared reflectance spectroscopy (NIRS) for predicting total carotenoid, lutein and β-carotene contents in skin and flesh of Cucurbita pepo fruits was assessed. The carotenoid contents were performed by HPLC, and were regressed against different spectral transformations by modified partial least square (PLSm) regression. Coefficients of determination in the external validation varied from 0.81 to 0.96, which characterize those equations as having from good to excellent quantitative information. The standard deviation (SD) to standard error of prediction ratio (RPD) and range to standard error of prediction ratio (RER) were variable for the different fruit part and compounds, and showed values that were characteristic of equations suitable for screening purposes. PLSm loading plots corresponding to the first terms of the equations showed that effects of the C–H group of starch and lipids, O–H group of water, as well as protein and chlorophyll, were most important in modeling prediction equations. The use of NIRS represents an important breakthrough in breeding for improved nutritional quality of summer squash fruit.

Prediction of Chemical Composition in Distillers Dried Grain with Solubles and Corn Using Real-Time Near-Infrared Reflectance Spectroscopy

This work was conducted to assess the use of Near-infrared reflectance spectroscopy (NIRS) as a technique to analyze nutritional constituents of Distillers dried grain with solubles (DDGS) and corn quickly and accurately, and to apply an NIRS-based indium gallium arsenide array detector, rather than a NIRS-based scanning system, to collect spectra and induce and analyze calibration equations using equipment which is better suited to field application. As a technique to induce calibration equations, Partial Least Squares (PLS) was used, and for better accuracy, various mathematical transformations were applied. A multivariate outlier detection method was applied to induce calibration equations, and, as a result, the way of structuring a calibration set significantly affected prediction accuracy. The prediction of nutritional constituents of distillers dried grains with solubles resulted in the following: moisture (<TEX>$R^2$</TEX>=0.80), crude protein (<TEX>$R^2$</TEX>=0.71), crude fat (<TEX>$R^2$</TEX>=0.80), crude fiber (<TEX>$R^2$</TEX>=0.32), and crude ash (<TEX>$R^2$</TEX>=0.72). All constituents except crude fiber showed good results. The prediction of nutritional constituents of corn resulted in the following: moisture (<TEX>$R^2$</TEX>=0.79), crude protein (<TEX>$R^2$</TEX>=0.61), crude fat (<TEX>$R^2$</TEX>=0.79), crude fiber (<TEX>$R^2$</TEX>=0.63), and crude ash (<TEX>$R^2$</TEX>=0.75). Therefore, all constituents except for crude fat and crude fiber were predicted for their chemical composition of DDGS and corn through Near-infrared reflectance spectroscopy.

Identifying variation in the nutritional value of corn based on chemical kernel characteristics

The metabolisable energy of corn can vary by more than 400 kcal/kg in poultry. Chemical, rather than physical kernel characteristics may be more accurate for predicting nutrient and energy availability. Factors that affect variability include genetics, agronomic conditions, proximate composition, pre- and post-harvest processing variables, and the presence of anti-nutritional factors. Variation in the nutritional value of corn may be better identified when the relationship between protein and starch are considered rather than by total content or predicted digestibility values alone. Recently, the use of near-infrared reflectance spectroscopy has made rapid valuation of corn based on chemical kernel characteristics a possibility. Prediction equations may be useful for formulating diets to more closely approximate actual metabolisable energy content for poultry.