Near-infrared Spectroscopy Calibration Models Research Articles

Evaluating NIRS for predicting faecal fibre, protein fractions and digestibility in dairy sheep and goats fed alfalfa hay and concentrate

This study evaluated the effectiveness of near-infrared spectroscopy (NIRS) in predicting faecal fibre, protein fractions and total tract apparent digestibility in dairy sheep and goats fed varying ratios of alfalfa-hay and concentrate. A total of 180 faecal samples from dairy sheep and goats were collected and chemically analysed. Faecal samples underwent also NIRS spectral acquisition and calibration models were obtained through modified partial least square regression and tested for robustness. The NIRS provided excellent estimations for N and CP as % as is (R 2 ExV 0.91 and 0.91, RPD 3.16 and 3.33, respectively), good estimation for N, CP and ND soluble CP % DM (R 2 ExV 0.90, 0.90 and 0.85, RPD 3.14, 2.90 and 2.48) and ND soluble CP expressed as % as is (R 2 ExV 0.87, RPD 2.55), approximative quantitative prediction for acid detergent insoluble crude protein (ADICP) and ADIN % acid detergent fibre (ADF) (R 2 ExV 0.79 and 0.71, RPD 1.93 and 1.78, respectively) and ADICP as % as is (R 2 ExV 0.67, RPD 1.70) but resulted to be less effective when predicting all the other components. The prediction models for total-tract digestibility (ttD) of ADICP performed best among the developed equations (R 2 ExV 0.57, RPD 1.38), but the model was not sufficient for precise predictions, suggesting a good capacity of NIRS to predict faecal N excretion but also intrinsic limitations of faecal spectra due to the partial residual nutrients content, compared to the diet. Increasing the number of calibration samples and values, the forage species, and the F:C ratios in the diet offered to the animals could improve the NIRS performance.

Application of near-infrared spectroscopy for hay evaluation at different degrees of sample preparation.

A study was conducted to quantify the performance differences of the nearinfrared spectroscopy (NIRS) calibration models developed with different degrees of hay sample preparations. A total of 227 imported alfalfa (Medicago sativa L.) and another 360 imported timothy (Phleum pratense L.) hay samples were used to develop calibration models for nutrient value parameters such as moisture, neutral detergent fiber, acid detergent fiber, crude protein, and in vitro dry matter digestibility. Spectral data of hay samples prepared by milling into 1-mm particle size or unground were separately regressed against the wet chemistry results of the abovementioned parameters. The performance of the developed NIRS calibration models was evaluated based on R2, standard error, and ratio percentage deviation (RPD). The models developed with ground hay were more robust and accurate than those with unground hay based on calibration model performance indexes such as R2 (coefficient of determination), standard error, and RPD. Although the R2 of calibration models was mainly greater than 0.90 across the feed value indexes, the R2 of cross-validations was much lower. The R2 of cross-validation varies depending on feed value indexes, which ranged from 0.61 to 0.81 in alfalfa, and from 0.62 to 0.95 in timothy. Estimation of feed values in imported hay can be achievable by the calibrated NIRS. However, the NIRS calibration models must be improved by including a broader range of imported hay samples in the modeling. Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparations compromising analysis accuracy by NIRS.

Use of near-infrared spectroscopy to estimate physical, anatomical and hydraulic properties of Eucalyptus wood.

Tree breeding programs and wood industries require simple, time- and cost-effective techniques to process large volumes of samples. In recent decades, near-infrared spectroscopy (NIRS) has been acknowledged as one of the most powerful techniques for wood analysis, making it the most used tool for high-throughput phenotyping. Previous studies have shown that a significant number of anatomical, physical, chemical and mechanical wood properties can be estimated through NIRS, both for angiosperm and gymnosperm species. However, the ability of this technique to predict functional traits related to drought resistance has been poorly explored, especially in angiosperm species. This is particularly relevant since determining xylem hydraulic properties by conventional techniques is complex and time-consuming, clearly limiting its use in studies and applications that demand large amounts of samples. In this study, we measured several wood anatomical and hydraulic traits and collected NIR spectra in branches of two Eucalyptus L'Hér species. We developed NIRS calibration models and discussed their ability to accurately predict the studied traits. The models generated allowed us to adequately calibrate the reference traits, with high R2 (≥0.75) for traits such as P12, P88, the slope of the vulnerability curves to xylem embolism or the fiber wall fraction, and with lower R2 (0.39-0.52) for P50, maximum hydraulic conductivity or frequency of ray parenchyma. We found that certain wavenumbers improve models' calibration, with those in the range of 4000-5500cm-1 predicting the highest number of both anatomical and functional traits. We concluded that the use of NIRS allows calibrating models with potential predictive value not only for wood structural and chemical variables but also for anatomical and functional traits related to drought resistance in wood types with complex structure as eucalypts. These results are promising in light of the required knowledge about species and genotypes adaptability to global climatic change.

Adaptive Artificial Neural Network in near infrared spectroscopy for standard-free calibration transfer

Near infrared spectroscopy (NIRS) is a secondary analytical method that could use machine learning to predict components of interest for various applications. Calibration transfer (CT) algorithms have been used to transfer the existing NIRS calibration model to another NIRS instruments to reduce the involvement of expensive and tedious conventional chemical analytical methods. However, most of CT algorithms involve both primary and secondary instruments to acquire NIRS signals from standard samples for spectral standardization. On the other hand, artificial neural network (ANN) that can adapt to new environmental conditions may be an alternative standard-free CT algorithm in transferring a trained ANN to secondary instruments without the involvement of the primary instrument. Although ANN has been widely implemented in NIRS research as a calibration model, its feasibility as an alternative CT method has not been evaluated. Thus, this study evaluates the feasibility of an adaptive ANN (AANN) in transferring ANN model from primary to secondary instruments using two traceable NIRS datasets. First, ANN and ANN with principal components (PCs-ANN) were developed and optimized using Bayesian learning algorithm. After that, these ANNs were adapted to secondary instruments using respective transfer data, in which, only the weights and biases of the ANNs were updated. Findings show that the lowest averaged RMSEP was achieved by the proposed PCs-AANN and AANN in the corn and wheat datasets, respectively. Particularly, the computation cost of having a calibration model in secondary instruments has been substantially reduced by means of the proposed AANN algorithm.

Multispecies, multisite, multi-age PLS regression models of chemical properties of eucalypts wood using Fourier Transformed near-Infrared (FT-NIR) spectroscopy

Near Infrared Spectroscopy (NIR) is often used to perform high throughput phenotyping on thousands of genotypes using prediction models with high variability. A study was therefore undertaken to analyze the potential of multispecies, multisite and multi-age NIR calibration models of seven chemical properties of eucalyptus wood. The models are based on 358 samples selected among more than 5000 samples that belong to five eucalypt species including hybrids. The samples were collected from trees aged 2-35 originating from four different countries. Spectra were measured on non-extracted wood powders using an FT-NIR spectrometer. Models were established in the spectral range of 9090-4040 cm−1 using the PLS regression method, tested by repeated cross-validation and validated on independent test sets. The results showed that the robust models for total extractives (R2 P = 0.91, RMSEP = 1.20%, RPD = 3.3) and KL (R2 P = 0.89, RMSEP = 1.21%, RPD = 3.0) provided good predictions. These two properties were the best predicted, followed by the S/G ratio (R2 P = 0.84, RMSEP = 0.19, RPD = 2.5) and ASL content (R2 P = 0.81, RMSEP of 0.54, RPD = 2.3). For holocellulose, alphacellulose, and hemicelluloses contents, the models provided approximate predictions. The prediction errors were always less than twice of the laboratory errors except for ASL and S/G ratio. For total extractives and ASL, β-coefficients of models were of approximately the same magnitude throughout the 9000-4000 cm−1 region while for the five other properties, they were higher in the 7500-4000 cm−1 region. Models were also established in narrower NIR regions, and the quality of models obtained was about the same as that of the models based in the 9090-4000 cm−1 wide range. These established robust models can be used to make predictions based on samples of high variability.

Application of Online NIR Spectroscopy to Enhance Process Understanding and Enable In-process Control Testing of Secondary Drying Process for a Spray-dried Solid Dispersion Intermediate

Near infrared spectroscopy (NIRS) was utilized to determine the endpoint of secondary drying process (post primary spray drying) of Spray-Dried Intermediates (SDI). In addition, NIR methods have been developed to quantify residual solvents (acetone and water), which are in-process controls (IPCs), and assay on the spray dried intermediate, thereby minimizing the need for off-line sample testing. NIRS calibration models were built with Partial Least Squares (PLS) regression for samples from several statistically designed experiments. Standard errors of prediction (SEP) of 0.1 wt. % for acetone, 0.2 wt. % for water, and 3.0 mg API/g SDI for API potency were obtained from validation of the models. When these methods were transferred to commercial scale on a different analyzer at a different site, additional updates to the NIR models were successfully made to overcome the impact from the differences in instrumentation and scale. Not only could real-time, in-process release be achieved, but also consistency and quality of data could be improved by minimizing or eliminating sample handling issues for off-line sample analysis. These NIR methods for secondary drying might also be used to optimize the drying process cycle time and study the effect of agitation rate, jacket temperature, and drying gas sweep rate on drying cycle time.

Evaluation of Near Infrared Spectroscopy (NIRS) for Estimating Soil Organic Matter and Phosphorus in Mediterranean Montado Ecosystem

The Montado is an agro-silvo-pastoral ecosystem characteristic of the Mediterranean region. Pasture productivity and, consequently, the possibilities for intensifying livestock production depend on soil fertility. Soil organic matter (SOM) and phosphorus (P2O5) are two indicators of the evolution of soil fertility in this ecosystem. However, their conventional analytical determination by reference laboratory methods is costly, time consuming, and laborious and, thus, does not meet the needs of current production systems. The aim of this study was to evaluate an alternative approach to estimate SOM and soil P2O5 based on near infrared spectroscopy (NIRS) combined with multivariate data analysis. For this purpose, 242 topsoil samples were collected in 2019 in eleven fields. These samples were subjected to reference laboratory analysis and NIRS analysis. For NIRS, 165 samples were used during the calibration phase and 77 samples were used during the external validation phase. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantification of these soil parameters. The coefficient of determination (R2, 0.85 for SOM and 0.76 for P2O5) and the residual predictive deviation (RPD, 2.7 for SOM and 2.2 for P2O5) obtained in external validation indicated the potential of NIRS to estimate SOM and P2O5, which can facilitate farm managers’ decision making in terms of dynamic management of animal grazing and differential fertilizer application.

Near-Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures

Dryland pastures provide the basis for animal sustenance in extensive production systems in Iberian Peninsula. These systems have temporal and spatial variability of pasture quality resulting from the diversity of soil fertility and pasture floristic composition, the interaction with trees, animal grazing, and a Mediterranean climate characterized by accentuated seasonality and interannual irregularity. Grazing management decisions are dependent on assessing pasture availability and quality. Conventional analytical determination of crude protein (CP) and fiber (neutral detergent fiber, NDF) by reference laboratory methods require laborious and expensive procedures and, thus, do not meet the needs of the current animal production systems. The aim of this study was to evaluate two alternative approaches to estimate pasture CP and NDF, namely one based on near-infrared spectroscopy (NIRS) combined with multivariate data analysis and the other based on the Normalized Difference Vegetation Index (NDVI) measured in the field by a proximal active optical sensor (AOS). A total of 232 pasture samples were collected from January to June 2020 in eight fields. Of these, 96 samples were processed in fresh form using NIRS. All 232 samples were dried and subjected to reference laboratory and NIRS analysis. For NIRS, fresh and dry samples were split in two sets: a calibration set with half of the samples and an external validation set with the remaining half of the samples. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantifying pasture quality parameters, with greater accuracy in dry samples (R2 = 0.936 and RPD = 4.01 for CP and R2 = 0.914 and RPD = 3.48 for NDF) than fresh samples (R2 = 0.702 and RPD = 1.88 for CP and R2 = 0.720 and RPD = 2.38 for NDF). The NDVI measured by the AOS shows a similar coefficient of determination to the NIRS approach with pasture fresh samples (R2 = 0.707 for CP and R2 = 0.648 for NDF). The results demonstrate the potential of these technologies for estimating CP and NDF in pastures, which can facilitate the farm manager’s decision making in terms of the dynamic management of animal grazing and supplementation needs.

Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem

Pasture quality monitoring is a key element in the decision making process of a farm manager. Laboratory reference methods for assessing quality parameters such as crude protein (CP) or fibers (neutral detergent fiber: NDF) require collection and analytical procedures involving technicians, time, and reagents, making them laborious and expensive. The objective of this work was to evaluate two technological and expeditious approaches for estimating and monitoring the evolution of the quality parameters in biodiverse Mediterranean pastures: (i) near infrared spectroscopy (NIRS) combined with multivariate data analysis and (ii) remote sensing (RS) based on Sentinel-2 imagery to calculate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). Between February 2018 and March 2019, 21 sampling processes were carried out in nine fields, totaling 398 pasture samples, of which 315 were used during the calibration phase and 83 were used during the validation phase of the NIRS approach. The average reference values of pasture moisture content (PMC), CP, and NDF, obtained in 24 tests carried out between January and May 2019 in eight fields, were used to evaluate the RS accuracy. The results of this study showed significant correlation between NIRS calibration models or spectral indices obtained by remote sensing (NDVIRS and NDWIRS) and reference methods for quantifying pasture quality parameters, both of which open up good prospects for technological-based service providers to develop applications that enable the dynamic management of animal grazing.

Models for predicting the within-tree and regional variation of tracheid length and width for plantation loblolly pine

AbstractLoblolly pine is a major fibre source for the pulp and paper industry. Here we developed the first nonlinear models to predict the within-tree and regional variation of tracheid length and width for planted loblolly pine. Data were obtained from macerated tracheids and near-infrared spectroscopy calibration models from trees sampled in 109 stands across the southeastern United States. The fixed effects for the final tracheid length model, which included cambial age, height of disk within tree, and physiographic region, explained 71 percent of the variation with root mean square error (RMSE) of 0.28 mm, while the fixed effects for the final tracheid width model explained 57 percent of the variation with RMSE of 1.4 μm. There was significant variation in tracheid properties across the growing regions. Tree maps showing within-tree variability in tracheid properties were produced. Five simulated scenarios were compared using the models developed, with mean tracheid dimensions calculated on a whole-tree basis at a first and second thinnings, and at final harvest. Also from the final harvest, the tops of trees, and outerwood chips produced during lumber manufacturing were also simulated. For the whole tree scenarios, both mean tracheid length and width increased with age, increasing from 2.24 mm and 40.5 μm (age 12), to 2.51 mm and 41.3 μm (age 18), and to 2.73 and 41.8 μm at age 25, respectively. The tops of the trees at age 25 had a mean tracheid length of 2.46 mm and a mean width of 41.0 μm, while the chips had a mean tracheid length of 3.13 mm and a mean width of 42.5 μm. Due to the models representing samples collected from across the southeastern United States, and their relatively high precision, they are suitable for incorporation into growth and yield systems allowing for prediction of tracheid properties.

Simultaneous determination of antioxidant properties and total phenolic content of Siraitia grosvenorii by near infrared spectroscopy

This study was conducted to evaluate the feasibility of using near-infrared spectroscopy (NIRS) for simultaneous determination of total phenolic content (TPC) and antioxidant properties of Siraitia grosvenorii, specifically 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), ferric reducing antioxidant power (FRAP), and 2,2-diphenyl-1-picrylhydrazyl (DPPH). Partial least squares (PLS) coupled with wavelength selection was applied to develop NIRS calibration models based on a set of 80 samples. All models established were cross- and test set validated. The root mean square error of cross validation (RMSECV) and the coefficient of determination for cross validation $$\left( {{\text{R}}_{{{\text{cv}}}}^{2} } \right) $$ were calculated to evaluate the performance of models. The results showed good precision of models for TPC ($${\text{R}}_{{{\text{cv}}}}^{2}$$ = 0.9829, RMSECV = 0.0426), DPPH ($${\text{R}}_{{{\text{cv}}}}^{2}$$ = 0.9477, RMSECV = 0.0279), ABTS ($${\text{R}}_{{{\text{cv}}}}^{2}$$ = 0.8958, RMSECV = 0.0154), and FRAP ($${\text{R}}_{{{\text{cv}}}}^{2}$$ = 0.9137, RMSECV = 0.0176). In addition, 20 unknown samples were used to evaluate the predictive ability of the obtained models, and the ratio performance deviation (RPD) for TPC, ABTS, DPPH, and FRAP ranged from 2.70 to 4.57, which indicate that these models had acceptable predictive ability. These results demonstrated that NIRS has the potential to be a time saving and cost-effective method for the determination of both TPC and antioxidant capacities of Siraitia grosvenorii.

Fine root lignin content is well predictable with near-infrared spectroscopy

1. Root lignin is a key driver of root decomposition, which in turn is a fundamental component of the terrestrial carbon cycle and increasingly in the focus of ecologists and global climate change research. However, measuring lignin content is labor-intensive and therefore not well-suited to handle the large sample sizes of most ecological studies. To overcome this bottleneck, we explored the applicability of high-throughput near infrared spectroscopy (NIRS) measurements to predict fine root lignin content. 2. We measured fine root lignin content in 73 plots of a field biodiversity experiment containing a pool of 60 grassland species using the Acetylbromid (AcBr) method. To predict lignin content, we established NIRS calibration and prediction models based on partial least square regression (PLSR) resulting in moderate prediction accuracies (RPD = 1.96, R2 = 0.74, RMSE = 3.79). 3. In a second step, we combined PLSR with spectral variable selection. This considerably improved model performance (RPD = 2.67, R2 = 0.86, RMSE = 2.78) and enabled us to identify chemically meaningful wavelength regions for lignin prediction. 4. We identified 38 case studies in a literature survey and quantified median model performance parameters from these studies as a benchmark for our results. Our results show that the combination Acetylbromid extracted lignin and NIR spectroscopy is well suited for the rapid analysis of root lignin contents in herbaceous plant species even if the amount of sample is limited.

Construction of global and robust near-infrared calibration models based on hybrid calibration sets using Partial Least Squares (PLS) regression

Near-infrared spectroscopy (NIR) models built on a particular instrument are often invalid on other instruments due to spectral inconsistencies between the instruments. In the present work, global and robust NIR calibration models were constructed by partial least square (PLS) regression based on hybrid calibration sets, which are composed of both primary and secondary spectra. Three datasets were used as case studies. The first consisted of 72 radix scutellaria samples measured on two NIR spectrometers with known baicalin content. The second was composed of 80 corn samples measured on two instruments with known moisture, oil, and protein concentrations. The third dataset included 279 primary samples of tobacco with known nicotine content and 78 secondary samples of tobacco with known nicotine concentrations. The effect of the number of secondary spectra in the hybrid calibration sets and the methods for selecting secondary spectra on the PLS model performance were investigated by comparing the results obtained from different calibration sets. This study shows that the global and robust calibration models accurately predicted both primary and secondary samples as long as the ratios of the number of primary spectra to the number of secondary spectra were less than 22. The models performance was not influenced by the selection method of the secondary spectra. The hybrid calibration sets included the primary spectral information and also the secondary spectra; information, rendering the constructed global and robust models applicable to both primary and secondary instruments.

A near-infrared reflectance spectroscopic method for the direct analysis of several fodder-related chemical components in drumstick (Moringa oleifera Lam.) leaves.

The drumstick tree has traditionally been used as foodstuff and fodder in several countries. Due to its high nutritional value and good biomass production, interest in this plant has increased in recent years. It has therefore become important to rapidly and accurately evaluate drumstick quality. In this study, we addressed the optimization of Near-infrared spectroscopy (NIRS) to analyze crude protein, crude fat, crude fiber, iron (Fe), and potassium (K) in a variety of drumstick accessions (N=111) representing different populations, cultivation programs, and climates. Partial least-squares regression with internal cross-validation was used to evaluate the models and identify possible spectral outliers. The calibration statistics for these fodder-related chemical components suggest that NIRS can predict these parameters in a wide range of drumstick types with high accuracy. The NIRS calibration models developed in this study will be useful in predicting drumstick forage quality for these five quality parameters.

Accuracy of Near Infrared Spectroscopy to Predict Quality of Pork and Pork Products Including Samples of Krškopolje and Turopolje Pigs

14.00 Normal 0 21 false false false SL X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Calibri; mso-bidi-theme-font:minor-latin; mso-ansi-language:EN-GB; mso-fareast-language:EN-US;} Study demonstrates the preliminary results of the evaluation of pork and pork products of local Slovenian (Krskopolje) and Croatian (Turopolje) pig breeds using near infrared spectroscopy (NIRS) conducted in the frame of European Union H2020 project TREASURE. For that purpose, samples from meat and products of two local pig breeds were collected, scanned with near infrared spectroscopy apparatus and chemically analysed (for proximate composition, fatty acids composition, proteolysis index, salt content and water activity). Data obtained were added to the database of previously collected samples and prediction models were recalibrated and reassessed. In general, NIRS calibration models are considered to be fit for purpose when the requirements (chemometric parameters) for screening purposes are met. In the present study, the quality of recalibrations using the samples from local pig breeds confirmed practical applicability for majority of studied quality traits. Further efforts are needed to enlarge the database with additional samples from local pig breeds to improve the robustness of the models and to test the calibrations on the independent sets of samples (i.e. with external validation). Normal 0 false false false SL X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Navadna tabela; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-fareast-language:EN-US;}

Determination of glomalin in agriculture and forest soils by near-infrared spectroscopy

Determining and characterizing soil organic matter (SOM) cheaply and reliably can help to support decisions concerning sustainable land management and climate policy. Glomalin was recommended as one of possible indicators of SOM quality. Extracting glomalin from and determining it in soils using classical chemical methods is too complicated and therefore near-infrared spectroscopy (NIRS) was studied as a method of choice for the determination of glomalin. Representative sets of 84 different soil samples from arable land and grasslands and 75 forest soils were used to develop NIRS calibration models. The parameters of the NIRS calibration model (R = 0.90 for soils from arable land and grasslands and R = 0.94 for forest soils) proved that glomalin can be determined in air-dried soils by NIRS with adequate trueness and precision simultaneously with determination of nitrogen and oxidizable carbon.

The potential of NIRs as a non-destructive post-harvest quality assessment tool for ‘Triumph’ persimmon fruit

The potential of near-infrared spectroscopy (NIRs) as an alternative non-destructive post-harvest quality assessment tool for ‘Triumph’ persimmon fruit was assessed. Fruits were harvested at four stages of maturity and each stage was replicated four times. Fruit quality was assessed at harvest, after storage and after shelf-life. Spectra data were obtained by scanning three positions on each fruit: green equatorial, yellow equatorial and the stylar end using a multi-purpose analyzer (MPA) spectrometer. Reference data for flesh firmness, total soluble solids (TSS), titratable acidity (TA) and fruit colour were determined using conventional tools on the same fruits from which NIR spectra were obtained. Reference data confirmed that a colour chart cannot categorize fruits into precise distinctive quality classes at harvest. In both the 2012 and 2013 seasons, all measured parameters had overlapping quality ranges across maturity stages. NIRs calibration and validation models showed that NIRs can predict TSS and fruit colour throughout post-harvest storage of ‘Triumph’ persimmon fruit. Furthermore, NIRs has shown some potential to predict firmness and TA by scanning certain fruit positions after storage and after shelf-life. NIRs can therefore be an alternative to the destructive TSS and TA quality assessment tools.

The potential of near infrared spectroscopy (NIRS) to measure the chemical composition of aquaculture solid waste

In aquaculture, it is extremely important to determine the composition of fish farm waste to evaluate its potential impacts and to improve its reuse. Near-infra red spectroscopy (NIRS), an alternative to standard chemical analytical techniques, is a quick non-invasive method to assess physical and chemical composition, reducing the cost of routine analysis. We developed NIRS calibration models for organic matter (OM), total organic carbon (TOC), total organic nitrogen (TON), the carbon/nitrogen ratio (C/N), total phosphorus (TP) and the lipid content of marine fish particulate waste. To obtain a wide range of compositions of fish waste, decomposition time, feed loss, and inter-specific variations were taken into account. The NIRS calibration models were built using three sub-datasets: in Scenario 1) the calibration was species-specific, including only seabass waste (SeabassWaste), in Scenario 2), the calibration included data from two other species (MultiSpeciesWaste) and in Scenario 3), the general calibration included all data as well as simulation of extreme feed loss (up to 50%) (Faeces&Feed). All calibrations performed using either dried or wet samples gave equations with high coefficients of determination (R2) and reasonably low standard error of cross validation (SECV) values for all parameters tested, except for TP due the high proportion of mineral P.

Determination of the Mineral Composition and Toxic Element Contents of Propolis by Near Infrared Spectroscopy.

The potential of near infrared spectroscopy (NIR) with remote reflectance fiber-optic probes for determining the mineral composition of propolis was evaluated. This technology allows direct measurements without prior sample treatment. Ninety one samples of propolis were collected in Chile (Bio-Bio region) and Spain (Castilla-León and Galicia regions). The minerals measured were aluminum, calcium, iron, potassium, magnesium, phosphorus, and some potentially toxic trace elements such as zinc, chromium, nickel, copper and lead. The modified partial least squares (MPLS) regression method was used to develop the NIR calibration model. The determination coefficient (R2) and root mean square error of prediction (RMSEP) obtained for aluminum (0.79, 53), calcium (0.83, 94), iron (0.69, 134) potassium (0.95, 117), magnesium (0.70, 99), phosphorus (0.94, 24) zinc (0.87, 10) chromium (0.48, 0.6) nickel (0.52, 0.7) copper (0.64, 0.9) and lead (0.70, 2) in ppm. The results demonstrated that the capacity for prediction can be considered good for wide ranges of potassium, phosphorus and zinc concentrations, and acceptable for aluminum, calcium, magnesium, iron and lead. This indicated that the NIR method is comparable to chemical methods. The method is of interest in the rapid prediction of potentially toxic elements in propolis before consumption.

Strategies for Selecting the Calibration Set in Pharmaceutical Near Infrared Spectroscopy Analysis. A Comparative Study

Introduction In recent years, the simplicity and expeditiousness of near infrared spectroscopy (NIRS) have substantially fostered its use for the pharmaceutical analysis. Developing appropriate NIRS calibration models requires careful selection of calibration sets containing all potential sources of variability in the production samples to be analysed. Once the whole variability is included, the predictive capacity of the calculated models is enhanced and those can be considered useful in the quality control. In this work, we assessed three different calibration strategies for the quantification of the active pharmaceutical ingredient (API) in a pharmaceutical granulate in low concentration (10 mg g−1). Such strategies were used to construct calibration models, allowing all potential variability in new, unknown samples to be considered.