Ratio Performance Deviation Research Articles

Analyses of phosmet residues in apples with surface-enhanced Raman spectroscopy

Residual pesticides such as phosmet in fruits and vegetables are one of the major food safety concerns for consumers around the world. Surface-enhanced Raman spectroscopy (SERS) with gold-coated substrates was exploited to analyze phosmet (0.5–10 μg/g) in apples. Phosmet could be detected from its SESR spectra at as low as 1 μg/mL in standard solution and 1 μg/g in apple extracts. The calculated limit of detection for phosmet in standard solution and in apple extracts were 1.01 μg/mL and 1.44 μg/g, respectively. There were linear relationships between the concentrations of phosmet solutions and the Raman intensities of four major characteristic peaks, with the coefficient of determination (R2) ranging from 0.905 to 0.984, the root mean square error of prediction (RMSEP) ranging from 0.46 to 1.16 μg/mL, and the ratio performance deviation (RPD) from 3.22 to 8.21. Although the linear regression results for phosmet in apple extracts with Raman intensities were not as good as those for the standard solutions (R2 = 0.866–0.945, RMSEP = 0.84–1.38 μg/g, RPD = 2.65–4.33, which still showed great potential of using SERS for quantitative analysis. The presence of non-targeted compounds in apple extracts affected the sensitivity for qualitative analysis and accuracy for quantitative analysis. This study demonstrated that SERS coupled with gold nanosubstrates could be used for analysis of trace contaminants like phosmet in complex food matrices.

Enhancing the applicability of near infrared spectroscopy for estimating specific gravity of green timber from Eucalyptus tereticornis by developing composite calibration using both radial and tangential face of wood

Specific gravity is a very important factor in determining the economics of wood and wood products. This study is aimed to apply the near infrared spectroscopy technique with integrating sphere and fiber optic probe accessories for fast prediction of specific gravity of green timber (moisture content 27–30 %) of Eucalyptus tereticornis from 10 to 12 year old plantation. Specific gravity (basic density) was determined by conventional method (oven dry wt/green volume) and correlated with near infrared spectra using partial least square (PLS) regression. Calibrations using radial face, tangential face and composite face (both radial and tangential) were developed and compared. Coefficient of determination of cross validation (\( {\text{r}}_{\text{cv}}^{ 2} \)) for radial-longitudinal, tangential-longitudinal solid strips samples and composite face (both radial and tangential) was between 0.74–0.77 and 0.64–0.77 using integrating sphere and fiber optic probe, respectively. The coefficient of determination of prediction (\( {\text{r}}_{\text{p}}^{ 2} \)) was between 0.73–0.85 (with integrating sphere) and 0.69–0.83 (with fiber optic probe). The ratio of performance deviation (RPD) ranges from 2.06 to 2.46 for all models. Root mean square error of prediction (RMSEP) was higher than root mean square error of cross validation (RMSECV). For composite models with radial and tangential face \( {\text{r}}_{\text{cv}}^{ 2} \)/\( {\text{r}}_{\text{p}}^{ 2} \) was 0.74/0.76 using integrating sphere and 0.71/0.83 with fiber optic probe with Quant 2 automatic factors optimization process. The results indicate that the composite model is just as good as the radial and tangential face models and can adequately address the problem where it is difficult to obtain perfect radial or tangential surface, particularly in small girth plantation timber or where surface recognition is difficult.

Fast and Inexpensive Detection of Total and Extractable Element Concentrations in Aquatic Sediments Using Near-Infrared Reflectance Spectroscopy (NIRS)

Adequate biogeochemical characterization and monitoring of aquatic ecosystems, both for scientific purposes and for water management, pose high demands on spatial and temporal replication of chemical analyses. Near-infrared reflectance spectroscopy (NIRS) may offer a rapid, low-cost and reproducible alternative to standard analytical sample processing (digestion or extraction) and measuring techniques used for the chemical characterization of aquatic sediments. We analyzed a total of 191 sediment samples for total and NaCl-extractable concentrations of Al, Ca, Fe, K, Mg, Mn, N, Na, P, S, Si, and Zn as well as oxalate- extractable concentrations of Al, Fe, Mn and P. Based on the NIR spectral data and the reference values, calibration models for the prediction of element concentrations in unknown samples were developed and tested with an external validation procedure. Except Mn, all prediction models of total element concentrations were found to be acceptable to excellent (ratio of performance deviation: RPD 1.8–3.1). For extractable element fractions, viable model precision could be achieved for NaCl-extractable Ca, K, Mg, NH4 +-N, S and Si (RPD 1.7–2.2) and oxalate-extractable Al, Fe and P (RPD 1.9–2.3). For those elements that showed maximum total values below 3 g kg−1 prediction models were found to become increasingly critical (RPD <2.0). Low concentrations also limited the performance of NIRS calibrations for extracted elements, with critical concentration thresholds <0.1 g kg−1 and 3.3 g kg−1 for NaCl and oxalate extractions, respectively. Thus, reliable NIRS measurements of trace metals are restricted to sediments with high metal content. Nevertheless, we demonstrated the suitability of NIRS measurements to determine a large array of chemical properties of aquatic sediments. The results indicate great potential of this fast technique as an analytical tool to better understand the large spatial and temporal variation of sediment characteristics in an economically viable way.

Near infrared spectroscopy for prediction of antioxidant compounds in the honey

The selection of antioxidant variables in honey is first time considered applying the near infrared (NIR) spectroscopic technique. A total of 60 honey samples were used to develop the calibration models using the modified partial least squares (MPLS) regression method and 15 samples were used for external validation. Calibration models on honey matrix for the estimation of phenols, flavonoids, vitamin C, antioxidant capacity (DPPH), oxidation index and copper using near infrared (NIR) spectroscopy has been satisfactorily obtained. These models were optimised by cross-validation, and the best model was evaluated according to multiple correlation coefficient (RSQ), standard error of cross-validation (SECV), ratio performance deviation (RPD) and root mean standard error (RMSE) in the prediction set. The result of these statistics suggested that the equations developed could be used for rapid determination of antioxidant compounds in honey.This work shows that near infrared spectroscopy can be considered as rapid tool for the nondestructive measurement of antioxidant constitutes as phenols, flavonoids, vitamin C and copper and also the antioxidant capacity in the honey.

Prediction of Soil Organic Carbon for Ethiopian Highlands Using Soil Spectroscopy

Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia. Soil samples were acquired from Ethiopia’s National Soil Testing Centre and direct field sampling. The reflectance of samples was measured using a FieldSpec 3 diffuse reflectance spectrometer. Outliers and sample relation were evaluated using principal component analysis (PCA) and models were developed through partial least square regression (PLSR). For nine watersheds sampled, 20% of the samples were set aside to test prediction and 80% were used to develop calibration models. Depending on the number of samples per watershed, cross validation or independent validation were used. The stability of models was evaluated using coefficient of determination (R2), root mean square error (RMSE), and the ratio performance deviation (RPD). The R2 (%), RMSE (%), and RPD, respectively, for validation were Anjeni (88, 0.44, 3.05), Bale (86, 0.52, 2.7), Basketo (89, 0.57, 3.0), Benishangul (91, 0.30, 3.4), Kersa (82, 0.44, 2.4), Kola tembien (75, 0.44, 1.9), Maybar (84. 0.57, 2.5), Megech (85, 0.15, 2.6), and Wondo Genet (86, 0.52, 2.7) indicating that the models were stable. Models performed better for areas with high SOC values than areas with lower SOC values. Overall, soil spectroscopy performance ranged from very good to good.

Multivariate calibration by near infrared spectroscopy for the determination of the vitamin E and the antioxidant properties of quinoa

Quinoa is a pseudocereal that is grown mainly in the Andes. It is a functional food supplement and ingredient in the preparation of highly nutritious food. In this paper we evaluate the potential of near infrared spectroscopy (NIR) for the determination of vitamin E and antioxidant capacity in the quinoa as total phenol content (TPC), radical scavenging activity by DPPH (2,2-diphenyl-2-picryl-hydrazyl) and cupric reducing antioxidant capacity (CUPRAC) expressed as gallic acid equivalent (GAE).For recording NIR a fiber optic remote reflectance probe applied directly on the quinoa samples without treatment was used. The regression method used was modified partial least squares (MPLS). The multiple correlation coefficients (RSQ) and the standard prediction error corrected (SEP(C)) were for the vitamin E (0.841 and 1.70mg100g−1) and for the antioxidants TPC (0.947 and 0.08mgGAEg−1), DPPH radical (0.952 and 0.23mgGAEg−1) and CUPRAC ( 0.623 and 0.21mgGAEg−1), respectively.The prediction capacity of the model developed measured by the ratio performance deviation (RPD) for vitamin E (2.51), antioxidants TPC (4.33), DPPH radical (4.55) and CUPRAC (1.55) indicated that NIRS with a fiber optic probe provides an alternative for the determination of vitamin E and antioxidant properties of the quinoa, with a lower cost, higher speed and results comparable with the chemical methods.

Comparison of NIR chemical imaging with conventional NIR, Raman and ATR-IR spectroscopy for quantification of furosemide crystal polymorphs in ternary powder mixtures

The aim of this study was to evaluate the ability of near-infrared chemical imaging (NIR-CI), near-infrared (NIR), Raman and attenuated-total-reflectance infrared (ATR-IR) spectroscopy to quantify three polymorphic forms (I, II, III) of furosemide in ternary powder mixtures. For this purpose, partial least-squares (PLS) regression models were developed, and different data preprocessing algorithms such as normalization, standard normal variate (SNV), multiplicative scatter correction (MSC) and 1st to 3rd derivatives were applied to reduce the influence of systematic disturbances. The performance of the methods was evaluated by comparison of the standard error of cross-validation (SECV), R2, and the ratio performance deviation (RPD). Limits of detection (LOD) and limits of quantification (LOQ) of all methods were determined. For NIR-CI, a SECVcorr-spec and a SECVsingle-pixel corrected were calculated to assess the loss of accuracy by taking advantage of the spatial information. NIR-CI showed a SECVcorr-spec (SECVsingle-pixel corrected) of 2.82% (3.71%), 3.49% (4.65%), and 4.10% (5.06%) for form I, II, III. NIR had a SECV of 2.98%, 3.62%, and 2.75%, and Raman reached 3.25%, 3.08%, and 3.18%. The SECV of the ATR-IR models were 7.46%, 7.18%, and 12.08%. This study proves that NIR-CI, NIR, and Raman are well suited to quantify forms I–III of furosemide in ternary mixtures. Because of the pressure-dependent conversion of form II to form I, ATR-IR was found to be less appropriate for an accurate quantification of the mixtures. In this study, the capability of NIR-CI for the quantification of polymorphic ternary mixtures was compared with conventional spectroscopic techniques for the first time. For this purpose, a new way of spectra selection was chosen, and two kinds of SECVs were calculated to achieve a better comparability of NIR–CI to NIR, Raman, and ATR-IR.

Determination of Pear Internal Quality Attributes by Fourier Transform Near Infrared (FT-NIR) Spectroscopy and Multivariate Analysis

This paper attempted the feasibility to determine firmness and soluble solid content (SSC) in intact pears using Fourier transform near infrared (FT-NIR) spectroscopy coupled with multivariate analysis. Principal component analysis and independent component analysis were employed comparatively to extract latent vectors from the original spectra data. Extreme learning machine (ELM) was performed to calibrate regression model. Some parameters of ELM model were optimized according to the lowest root mean square error of cross-validation in the calibration set. Moreover, the root mean square error of prediction of the calibration model was finally corrected for making it more closed to the true prediction error due to the effect of reference measurement error existing in the pear sample attribute value on the prediction error of the model. Experimental results showed that the \( R_p^2 \) and ratio performance deviation (RPD) in the prediction set were achieved as follows: \( R_p^2 \) = 0.81 and RPD = 2.28 for the firmness model when ICs = 6 and \( R_p^2 \) = 0.91 and RPD = 3.43 for the SSC model when ICs = 5. This study demonstrates that the predictive precision of the calibration model can be effectively enhanced in measurement of firmness and SSC in intact pears by use of FT-NIR spectroscopy combined with appropriate chemometrics methods.

Rapid Analysis of Apple Leaf Nitrogen using Near Infrared Spectroscopy and Multiple Linear Regression

A quick method was developed for diagnosis of nitrogen (N) in apple trees based on multiple linear regressions to establish the relationship between near-infrared reflectance spectra (NIRS) and the N contents of fresh and dry tissue. Spectral pretreatment methods such as derivatives, smoothing, and normalization were used. The derivatives appeared to be the most effective. The best calibration for fresh leaf gave 0.842 for the correlation coefficient of validation (Rv), 1.119 g kg−1 for the root mean square error of prediction (RMSEP), and 8.311 for the ratio of the range in reference data from the validation samples to the root mean square error of prediction (RER). The best calibration for dried ground samples was obtained with Rv = 0.952, RMSEP = 0.633 g kg−1, the ratio performance deviation (RPD) = 3.27, and RER = 13.728. The results showed that calibrations of dry-apple leaf are robust enough for an accurate prediction of N.

A rapid and consistent near infrared spectroscopic assay for biomass enzymatic digestibility upon various physical and chemical pretreatments in Miscanthus

Near infrared spectroscopy (NIRS) has been broadly applied as a quick assay for biological component and property analysis. However, NIRS remains unavailable for in-depth analysis of biomass digestibility in plants. In this study, NIRS was used to determine biomass enzymatic digestibility using 199 Miscanthus samples, which represents a rich germplasm resource and provides for a stable calibration model. The intensive evaluation indicates that the calibration and validation sets are comparable. Using the modified partial least squares method, seven optimal equations were generated with high determination coefficient on calibration (R2) at 0.75–0.89, cross-validation (R2cv) at 0.69–0.87, and the ratio performance deviation (RPD) at 1.80–2.74, which provide multiple options for NIRS prediction of biomass digestibility under different pretreatments. As biomass digestibility is a crucial parameter for biofuel processing, NIRS is a powerful tool for the high-throughput screening of biomass samples in plants.

NIR Spectroscopy: An Alternative for Soil Analysis

Advances in laboratory instrumentation and chemometrics provide alternatives to traditional methods of conducting soil chemical analysis. One of these is infrared diffuse reflectance spectroscopy in the near-infrared spectral range (NIRS). Herein we report the results of a multinational study to develop useful calibrations associating NIRS spectra with laboratory-measured results for total soil carbon (C), total soil nitrogen (N), δ13C, and δ15N from a single soil site in Mexico subjected to zero- and conventional-tillage regimens with and without crop residues and crop rotations of maize and wheat across 16 years. Modified partial least squares regression (MPLS) was used to obtain useful NIR predictions for total soil C and N, with ratio performance deviation (RPD) values of 6.8 and 2.6, respectively. Corresponding multiple correlation coefficients (RSQs) for C and N were 0.98 and 0.85, with standard errors of prediction (SEPs) of ±0.45 g C kg–1 and ±0.09g Nkg–1, respectively. The generation of δ15N and δ13C models produced different NIR recordings in soils with and without crop residues. Application of discriminant partial least squares (DPLS) statistics to the NIR spectral data allowed us to discriminate soils with and without residues. The prediction confidence for stable isotopes was 90% (internal validation) and 94% (external validation). Modified partial least squares regression was used to estimate δ15N and δ13C. Ratio performance deviation, RSQ, and SEP values obtained for δ13C and δ15N were 2.44 and 3.57, 0.83 and 0.81, ±0.5‰ (parts per thousand) and ±0.45‰ in soils with residues and 2.5 and 3.8, 0.93 and 0.92, and ±0.2‰ and ±0.23‰ in soils without residues, respectively. Overall, results obtained with NIRS were comparable to those obtained using conventional analytical methods, a finding that has wide relevance to agricultural soils and environmental studies in tropical locations. However, further testing is necessary to confirm that the calibration models are neither site nor instrument specific.

Reducing Sample Quantity and Maintaining High Prediction Quality of Grassland Biomass Properties with near Infrared Reflectance Spectroscopy

Broad-scale ecological research often suffers from insufficient spatial and temporal replication. Near infrared (NIR) reflectance spectroscopy offers the opportunity for rapid and cheap measurements of many chemical constituents in organic materials. However, standard NIR instrumentation requires a certain amount of sample material which strongly restricts the fields of application for the NIR technique. Therefore, we tested if reliable predictions from NIR spectra can be obtained utilising a device that reduces the amount of required sample material by more than 95% compared to standard equipment. For large and small sample quantities, we present two sets of calibration models for C, N, P, K, Ca and Mg concentrations as well as fibre components such as neutral detergent fibre (NDF), acid detergent fibre (ADF) and acid detergent lignin (ADL) in above-ground grassland community biomass. Coefficients of multiple determination ( R2) of calibration models based on spectral data derived from standard equipment for C, N, P, K, Ca, Mg, NDF, ADF and ADL were 0.78, 0.98, 0.78, 0.92, 0.87, 0.89, 0.95, 0.94 and 0.87, respectively. Except for C and P, the ratio of standard deviation of the reference values to the standard error of cross validation and ratio of performance deviation indicated acceptable to high model precision. The application of NIR spectroscopy for C and P measurements was limited due to low variation in concentrations and/or low concentrations in the analysed above-ground grassland biomass. As compared to the deviation of duplicate reference measurements, the standard error of prediction was less than two times higher for C, N, NDF, ADF, ADL and K and up to three times higher for P, Ca and Mg. Prediction models based on the spectral data recorded with a small sample cell (volume of sample material less than 0.25 cm3) were of similar precision. The significant reduction of sample material required for NIR analysis and, at the same time, maintaining (high) precision of calibration models is an important advance towards the wider adoption of the NIR technique in ecological research.

“Global” and “local” predictions of dairy diet nutritional quality using near infrared reflectance spectroscopy

The objective of the study was to evaluate performance of classic (global) and innovative (local) calibration techniques to monitor cattle diet, based on fecal near infrared reflectance spectroscopy (NIRS). A 3-yr on-farm survey (2005–2008) was carried out in Vietnam and La Reunion Island to collect animal, feed intake, and feces excretion data. Feed and feces were scanned by a Foss NIRsystem 5000 monochromator (Foss, Hillerød, Denmark) to estimate diet characteristics and nutrient digestibility. A data set including 1,322 diet-fecal pairs was built and used to perform global and local calibrations. Global equations gave satisfactory accuracy [coefficient of determination (R2) >0.8, 10% ≤ relative standard error of prediction (RSEP) ≤20%], whereas local equations gave good accuracy (R2 >0.8, RSEP <10%) or excellent accuracy (R2 >0.9, RSEP <10%) for the prediction of diet intake, quality, and digestibility. When validating the equations using the external individual data, both techniques were robust, with similar RSEP (8%) and R2 (0.82) values. The predictive performance of global and local equations was improved (RSEP = 5% and R2=0.90) when averaged animal data from farm, visit, and similar milk production were used. In particular, local equations reduced RSEP by 43% and increased R2 by 15%, on average, compared with those obtained from individual data. The low RSEP (4%), high R2 (0.96), and good ratio performance deviation (RPD=5) illustrated the excellent accuracy and robustness of the local equations. Findings suggest the ability of fecal NIRS to successfully and more accurately predict diet properties (intake, quality, and digestibility) with local calibration techniques compared with classic global techniques, especially on an averaged data set. Local calibration techniques represent an alternative promising method and potentially a decision support tool to decide whether diets meet dairy cattle requirements or need to be modified.

Mehlich 3 extractable nutrients as determined by near-infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) is a cost-effective and environmentally friendly technique of soil analysis that is particularly advantageous in intensive soil sampling and soil nutrient management as well. This study evaluated the potential of NIRS for predicting P, K, Ca, Mg, Cu, Zn, Mn, Fe, and Al extracted by Mehlich 3. We used 150 air-dried samples collected from a 15-ha site dominated by Orthic Humic Gleysol and Gleyed Dystric Brunisol soils. Calibration equations were developed using modified partial least squares regression. The accuracy of NIRS prediction was evaluated using the coefficient of determination (R2), the ratio of performance deviation (RPD), and the ratio of error range (RER). Reliable calibrations were found for Ca, Cu, and Mg (R2 ≥ 0.7, RPD ≥ 1.75, and RER ≥ 8). Less-reliable calibrations were found for Al, Fe, K, Mn, P, and Zn (R2 &lt; 0.7, RPD &lt; 1.75, and RER &lt; 8). In the validation with independent samples, acceptable regression coefficients (i.e., 0.8 ≤ slope ≤ 1.2) were only found for Ca, Mg, and Mn. We presumed that the pH of the Mehlich 3 extractant (2.5 ± 0.1) may affect the solubility of most of these nutrients, regardless the soil texture and, consequently, the potential of NIRS to predict them. The more a nutrient was correlated to clay content, the more it was likely predictable by NIRS. The prediction models obtained for Al, Ca, Cu, Fe, K, Mg, and Mn could still be used for screening purposes in cases where high accuracy is not required. These NIRS prediction models should be validated across larger geographic areas of geological homogeneity. Key words: Soil analysis, Mehlich 3, near-infrared reflectance spectroscopy, calibration

Near‐Infrared Reflectance Spectroscopy Prediction of Soil Properties: Effects of Sample Cups and Preparation

Most methods for soil analysis are based on wet chemistry. Near infrared reflectance spectroscopy (NIRS) is a cost‐effective and environmentally sound alternative technique. This study evaluated the effect of sample fineness (0.2, 0.5, 1, and 2 mm) and sample cups (transport versus spinning) on the accuracy of NIRS predictions of soil texture, cation‐exchange capacity, pH, total C and N, organic C, and potentially mineralizable N (Nmin) using 150 air‐dried samples collected from a 15‐ha site dominated by Humaquept, Endoaquept, and Dystrochrept soils. The best spectral pretreatment was determined for each property. Principal component analysis (PCA) was used to select samples in calibration and validation sets. Calibration equations were developed using the modified partial least square regression. The accuracy of NIRS prediction was evaluated using three statistics for the prediction set: coefficient of determination (R2), ratio of performance deviation (RPD), and ratio error range (RER). Across the factorial designed treatments, successful calibrations were observed for clay, sand, and Nmin (R2 ≥ 0.90, RPD ≥ 3, RER ≥ 15). Prediction accuracy of pH was poor (0.51 ≤ R2 ≤ 0.74, 1.39 ≤ RPD ≤ 1.92, 6.13 ≤ RER ≤ 8.33), while it was intermediate for remaining properties. Sample fineness of 2 mm appeared to be sufficient since finenesses of 0.2, 0.5, or 1.0 mm did not improve calibration accuracy. These findings at small scale should not be extrapolated and further investigations are required to validate them at a larger scale.

Opportunities for, and limitations of, near infrared reflectance spectroscopy applications in soil analysis: A review

Near infrared reflectance spectroscopy (NIRS) is a cost- and time-effective and environmentally friendly technique that could be an alternative to conventional soil analysis methods. In this review, we focussed on factors that hamper the potential application of NIRS in soil analysis. The reported studies differed in many aspects, including sample preparation, reference methods, spectrum acquisition and pre-treatments, and regression methods. The most significant opportunities provided by NIRS in soil analysis include its potential use in situ, the determination of various biological, chemical, and physical properties using a single spectrum per sample, and an estimated reduction of analytical cost of at least 50%. Contradictory results among studies on NIRS utilisation in soil analysis are partly related to variations in sample preparation and reference methods. The following calibration statistics appear to be most appropriate for comparing NIRS performance across soil attributes: (i) coefficient of determination (r2), (ii) ratio of performance deviation (RPD), (iii) coefficient of regression (b), and (iv) ratio of the standard error of prediction (SEP) to the standard error of the reference method (SER), i.e., the ratio of standard errors (RSE). Further investigations on issues such as (i) RSE guidelines, (ii) correlation between NIRS spectrophotometers, (iii) correlation of different reference methods for a given attribute to soil spectra, (iv) identification of key factors affecting the accuracy of NIRS predictions, and (v) efficient use of spectral libraries are required to enhance the acceptability of NIRS as a soil analysis technique and to make it more user-friendly. Standardized guidelines are proposed for the assessment of the accuracy of NIRS predictions of soil attributes.Key words: Near infrared reflectance spectroscopy, soil analysis, calibration

The natural abundance of 13C with different agricultural management by NIRS with fibre optic probe technology

In the present study the natural abundance of 13C is quantified in agricultural soils in Mexico which have been submitted to different agronomic practices, zero and conventional tillage, retention of crop residues (with and without) and rotation of crops (wheat and maize) for 17 years, which have influenced the physical, chemical and biological characteristics of the soil. The natural abundance of C13 is quantified by near infrared spectra (NIRS) with a remote reflectance fibre optic probe, applying the probe directly to the soil samples. Discriminate partial least squares analysis of the near infrared spectra allowed to classify soils with and without residues, regardless of the type of tillage or rotation systems used with a prediction rate of 90% in the internal validation and 94% in the external validation. The NIRS calibration model using a modified partial least squares regression allowed to determine the δ 13C in soils with or without residues, with multiple correlation coefficients 0.81 and standard error prediction 0.5‰ in soils with residues and 0.92 and 0.2‰ in soils without residues. The ratio performance deviation for the quantification of δ 13C in soil was 2.5 in soil with residues and 3.8 without residues. This indicated that the model was adequate to determine the δ 13C of unknown soils in the −16.2‰ to −20.4‰ range. The development of the NIR calibration permits analytic determinations of the values of δ 13C in unknown agricultural soils in less time, employing a non-destructive method, by the application of the fibre optic probe of remote reflectance to the soil sample.

Characterization of Maize Germplasm for the Chemical Composition of the Grain

The aim of this research was the evaluation of food grain quality-related traits in a collection of maize populations of different origins, currently used in the framework of breeding and genetic programs. A total of 1245 maize samples were scanned by near-infrared (NIR) spectroscopy to develop calibration equations to evaluate the content of crude protein, crude lipid, starch, and floatation area. The performances of the NIR equations developed in our study were assessed using the determination coefficient of cross-validation r(2) (ranging from 0.66 to 0.91) and the ratio of performance deviation (1.71-3.31) in flour starch and grain crude protein, respectively. Among the genotypes considered, 93 landraces belonging to the European Union Maize Landraces Core Collection (EUMLCC) were also analyzed for their content of lutein, zeaxanthin, and total carotenoids. Among the populations of the collection, several accessions, interesting from a nutritional point of view, were identified: VA25, VA158, VA282, VA284, VA285, VA567, VA572, VA814, VA950, VA1057, and VA1179. They showed protein and lipid contents ranging between 12.52 and 15.16% and 5.26 and 7.17%. The range of variation observed for antioxidants in the EUMLCC was quite large. Lutein varied between 1.03 and 21.00 mg kg(-1) dm, zeaxanthin varied between 0.01 and 35.00 mg kg(-1) dm, and total carotenoids ranged from 1.09 to 61.10 mg kg(-1) dm. Recently, a single cross-hybrid was developed from the ITA0370005 population; this hybrid had a high carotenoids content and is currently being used by the Italian food industry.

Near Infrared Spectroscopy Calibrations for Quantifying the Animal Species in Processed Animal Proteins

Near infrared (NIR) calibrations were developed for the instantaneous and simultaneous prediction of the animal species composition of processed animals proteins (PAPs). A total of 359 PAPs samples were used to built calibration and validation sets. Different derivatives and scatter correction pre-treatments were tested. Modified partial least squares regressions were developed for the prediction of the percentage of poultry by-products meal, pork meal, cattle meal, ruminant meal and non-ruminant meal in a mixture. All the equations presented R2 and ratio performance deviation ( RPD) values around 0.9 and 3, respectively. In the external validation procedure, the results obtained correspond well with the calibration statistics. This study shows the potential of NIR technology to quantify the animal species in PAPs and it clearly demonstrates the importance of the size and structure of the calibration set in the robustness of the equations.

Texture evaluation in cheeses by NIRS technology employing a fibre-optic probe

The use of NIRS (near infra-red spectroscopy) technology together with a remote-reflectance fibre-optic probe was studied for the analysis of ewe’s cheese texture. The cheeses were elaborated with milk from three breeds of sheep (Castellana, Churra and Assaf) with different somatic cell counts (SCC) (lower than 500,000 cells m L −1; between 1,000,000 and 1,500,000 cells m L −1, and more than 2,500,000 cells m L −1) and ripened for 12 months, thus 96 samples were obtained. At each ripening time NIRS spectra were recorded and instrumental texture were determined as Warner–Bratzler shear force using a TX-T2i plus. The regression method employed was modified partial least squares (MPLS). The calibration, with the multiple correlation coefficients (RSQ), 0.961 and standard error of prediction (SEP(C)) of 2.1 N, allows determining texture in the range 0.0–49.0 N. The ratio performance deviation (RPD) value obtained, 5.4, indicate that the NIRS equation obtained was applicable to unknown samples.