Range Error Ratio Research Articles

Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia

The objectives of this research were: (i) to assess the accuracy of diffuse reflectance spectroscopy (DRS) in predicting the soil organic carbon (SOC) content, and (ii) determine the importance of wavelength ranges and specific wavelengths in the SOC prediction model. The reflectance spectra of a total of 424 topsoils (0-25 cm) samples were measured in a laboratory using a portable Terra Spec 4 Hi-Res Mineral Spectrometer with a wavelength range 350-2500 nm. Partial least squares regression (PLSR) with leave-one-out cross validation was used to develop calibration models for SOC prediction. The accuracy of the estimate determined by the coefficient of determination ( R 2 ), the concordance correlation coefficient (ρc), the ratio of performance to deviation (RPD), the range error ratio (RER) and the root mean square error (RMSE) values of 0.83, 0.90, 2.42, 15.1 and 2.47 g C kg -1 respectively, indicated successful model for SOC prediction. The near infrared (NIR) and the short-wave infrared (SWIR) spectrums were more accurate than those in the visible (VIS) and short-wave near-infrared (SWNIR) spectral regions. The wavelengths contributing most to the prediction of SOC were at: 1925, 1915, 2170, 2315, 1875, 2260, 1910, 2380, 435, 1960, 2200, 1050, 1420, 1425 and 500 nm. This study has shown that VIS-NIR reflectance spectroscopy can be used as a rapid method for determining organic carbon content in the Red Mediterranean soils that can be sufficient for a rough screening.

Predicting Color Traits of Intact Broiler Breast Fillets Using Visible and Near-Infrared Spectroscopy

This study was conducted to assess the potential for visible and near-infrared (Vis/NIR) spectroscopy to predict an important quality attribute, color traits, of intact broiler breast fillets (pectoralis major). A total of 214 boneless and skinless chicken fillets were procured from a commercial processing plant. The quantitative calibration models were established between five color trait parameters (CIELAB L*, a*, b*, chroma, and hue angle) and spectra from four different spectral ranges (400–2500, 400–1100, 1100–2500, and 900–1700 nm) collected from skin (ventral) and bone (dorsal) sides of fillets individually by partial least squares regressions (PLSR). Predictive ability was assessed by coefficient of determination of prediction (R p 2), ratio performance deviation (RPD), and range error ratio (RER). Models developed based on spectra collected from fillet bone side performed better than those from skin side regardless of color trait, best results were all obtained using 400–2500 nm and closely followed by 400–1100 nm. Prediction results for meat redness a* (R p 2 = 0.81; RPD = 2.21; RER = 12.13) and hue angle (R p 2 = 0.80; RPD = 2.07; RER = 12.65) were the best and for L*, b*, and chroma (R p 2 ≥ 0.72; RPD ≥ 1.81; RER ≥ 8.48) were also well. This work systematically evaluated the influence of different color trait parameters, fillet side spectra, spectral ranges, and pre-treatment methods compared with previous investigation, and results suggest that Vis/NIR spectroscopy can be a feasible method for prediction of color traits of intact broiler breast fillets, especially when spectra are collected from the fillet bone side and range of optimal partial ranges is included.

Evaluation of intra-ring wood density profiles using NIRS: comparison with the X-ray method

Pith-to-bark wood density profiling is interesting in forestry science. By comparing it with the X-ray method, this study proved that a fiber optic NIR spectrometer with a high-precision displacement system could accurately measure intra-ring wood density with a spatial resolution of 0.5 mm. Most near-infrared spectroscopy (NIRS) studies for wood density determination use samples that have been pulverized beforehand. Attenuation of ionizing radiation is still the standard method to determine wood density with high spatial resolution. However, there is evidence that NIRS could be an accurate and affordable method for determining intra-ring density in solid wood strips. In this study, we research whether the results published for intra-ring density predictions in wood can be improved when calibrated with X-ray microdensitometry. The measurements were made using a fiber optic probe with a separation between measurement points of 0.508 mm in a range between 1200 and 2200 nm. A total of 4520 density points were used to create partial least squares regression (PLSR). X-ray densitometry data were used as reference values. Twenty PLSR calibrations were randomly executed on 31 samples collected from 28 Pinus radiata D. Don trees. Upon selecting 20 latent variables, the R 2 value was 0.873 for the training group and 0.895 for the validation group, while RMSEP values are 43.1 × 10−3 and 47.1 × 10−3 g cm−3 for the training and validation groups, respectively. The range error ratio (RER) was 13.7. The RER was high and almost in the range suggested for quantification purposes. Results are superior to wood density studies in the literature which do not employ spatial resolution and to those found in studies using hyperspectral imaging.

Application of near-infrared spectroscopy for estimation of non-structural carbohydrates in foliar samples of Eucalyptus globulus Labilladière.

Near-infrared reflectance spectroscopy (NIRS) is frequently used for the assessment of key nutrients of forage or crops but remains underused in ecological and physiological studies, especially to quantify non-structural carbohydrates. The aim of this study was to develop calibration models to assess the content in soluble sugars (fructose, glucose, sucrose) and starch in foliar material of Eucalyptus globulus. A partial least squares (PLS) regression was used on the sample spectral data and was compared to the contents measured using standard wet chemistry methods. The calibration models were validated using a completely independent set of samples. We used key indicators such as the ratio of prediction to deviation (RPD) and the range error ratio to give an assessment of the performance of the calibration models. Accurate calibration models were obtained for fructose and sucrose content (R2 > 0.85, root mean square error of prediction (RMSEP) of 0.95%–1.26% in the validation models), followed by sucrose and total soluble sugar content (R2 ~ 0.70 and RMSEP > 2.3%). In comparison to the others, calibration of the starch model performed very poorly with RPD = 1.70. This study establishes the ability of the NIRS calibration model to infer soluble sugar content in foliar samples of E. globulus in a rapid and cost-effective way. We suggest a complete redevelopment of the starch analysis using more specific quantification such as an HPLC-based technique to reach higher performance in the starch model. Overall, NIRS could serve as a high-throughput phenotyping tool to study plant response to stress factors.

Brachiaria plantaginea as a Potential (New) Source of Shikimic Acid. Quantification by NIR and PLS Regression

Shikimic acid is a natural organic compound produced in the biochemical pathways of eukaryotic organisms and is generally utilized as a starting material of the antiviral drug Oseltamivir (Tamiflu®). This study shows the results of shikimic acid accumulation in Brachiaria plantaginea, an abundant, grassy plant found in Brazil and other countries in Africa and America, after glyphosate spraying at three different doses. B. plantaginea plants harvested after 6 days of exposure to the herbicide showed that shikimic acid accumulation increased by 345 %, on average, compared to unsprayed plants. The combination of near infrared spectroscopy and multivariate analysis using partial least square regression was applied for the quantification of shikimic acid in B. plantaginea. Spectra of 44 samples were obtained using the diffuse reflectance mode in the range of 4000 to 10 000 cm−1 with 4 cm−1 resolution. Different mathematical pretreatments were applied to the spectra. As preprocessing, the data were mean-centered. The calibration model with seven factors on twenty-nine samples (training set) exhibited a coefficient of determination = 0.9930 and a standard error of calibration = 84.05. For external validation (11 samples on the test set), the coefficient of determination = 0.9317 and the standard error of prediction = 154.91. The percent calibration error range was 1 to 10 % for most of the samples and only two samples presented an error greater than 20 %. For external validation, the mean prediction error was 10 % and the range error ratio was 9.42, indicating that the model is qualified for screening calibration.

Assessment of the main plasma parameters included in a metabolic profile of dairy cow based on Fourier Transform mid-infrared spectroscopy: preliminary results.

BackgroundAlthough a metabolic profile represents a valid tool utilized in dairy herds to determine abnormalities in blood chemistry related to an increased risk of production diseases, there are no studies on application of Fourier Transform mid-infrared (FT-MIR) spectroscopy. This study assesses the potential application of FT-MIR to analyze the main blood biochemical parameters included in the metabolic profile of dairy cows. Infrared transmission spectra were acquired for 35 plasma samples (two replicates on each sample) of Italian Friesian dairy cows (14 primiparous and 21 pluriparous), all without clinical events, and at different stages of lactation, although mainly in the transition phase. Each sample was also analyzed independently using accepted reference clinical chemical methods and these results were used as calibrating values to perform predictive models by PLS method using cross validation.ResultsMeasured blood parameters concentrations were all within the reference ranges reported for healthy dairy cows. The number of extracted factors with the PLS procedure for each prediction model ranged between 3 and 7. The coefficient of determination (R2) of the prediction models ranged between 0.1 to values close to 1. R2 values greater than 0.9 were observed for the prediction models of total cholesterol, total protein, globulin, and albumin; values between 0.75 and 0.9 were observed for urea, NEFA, and total bilirubin, while values of R2 lower than 0.6 were observed for all minerals and for enzyme activity. The range error ratio (RER) and prediction to deviation (RPD) ranged from 5.1 to 43.8 and from 1 to 13.8 for RER and RPD, respectively. Values of RPD greater than 5 were observed for total cholesterol, total protein, albumin, and globulin. RPD ranged between 2 and 5 for the prediction models of urea, NEFA, and total bilirubin, while RPD and RER were low for minerals and enzyme activities.ConclusionsAlthough the results of this study require further validation, the use of FT-MIR spectroscopy was possible and provides fairly accurate measurement of various parameters of great importance in the evaluation of the metabolic and inflammatory status in dairy cows.

Multi-product calibration models using NIR spectroscopy

The physical–chemical composition of multiple biomasses can be predicted from one single calibration model instead of compositional prediction conducted by individual models. In this work, multi-product models, involving banana, coffee and coconut samples were built by partial least square regression (PLS) for ten different chemical constituents (total lignin, klason lignin, acid insoluble lignin, acid soluble lignin, extractives, moisture, ash, glucose, xylose and total sugars). The developed PLS models show satisfactory results, with relative error (RE%) less than 20.00, except for ash and xylose models; ratio performance deviation (RPD) values above 4.4 and range error ratio (RER) values above 4.00. This means that all models are qualified for screening calibration. Principal component analysis (PCA) was useful to demonstrate the possibility and the rationale for combining three biomass residues into one calibration model. The results have shown the potential of NIR in combination with chemometrics to quantify the chemical composition of feedstocks.

Vibrational spectroscopic methods for the overall quality analysis of washing powders

The aim of this study was to compare and evaluate the ability of near infrared- (NIR), Raman- and attenuated-total-reflection infrared (ATR-IR) spectroscopy as tools for the identification of washing powder brands as well as for an overall quantitative analysis of all ingredients of the analyzed laundry detergents. The laundry detergents used in this work were composed of 22 different ingredients.For this purpose, principal component analysis (PCA) cluster models and partial least-squares (PLS) regression models were developed and different data pre-processing algorithms such as standard-normal-variate (SNV), multiplicative scatter correction (MSC), first derivative BCAP (db1), second derivative smoothing (ds2), smoothing Savitzky Golay 9 points (sg9) as well as different normalization procedures such as normalization between 0 and 1 (n01), normalization unit length (nle) or normalization by closure (ncl) were applied to reduce the influence of systematic disturbances. The performance of the methods was evaluated by comparison of the number of principal components (PCs), regression coefficient (r), Bias, Standard error of prediction (SEP), ratio performance deviation (RPD) and range error ratio (RER) for each calibration model. For each of the 22 ingredients separate calibration models were developed. Raman spectroscopy was suitable for the analysis of only two ingredients (dye transfer inhibitor 1 and surfactant 6) and it was not possible to record all Raman spectra due to high fluorescence.NIR and ATR-IR are powerful methods to analyze washing detergents with low numbers of PCs being necessary, regression coefficients of only little below 1, small Biases and SEPs compared to the range and high RPDs and RERs

Rapid assessment of bioactive phenolics and methylxanthines in spent coffee grounds by FT-NIR spectroscopy

Spent coffee grounds (SCGs) are a great source of bioactive compounds with interest to pharmaceutical and cosmetic industries. Phenolics and methylxanthines are the main health related compounds present in SCG samples. Content estimation of these compounds in SCGs is of upmost importance in what concerns their profitable use by waste recovery industries.In the present work, near-infrared spectroscopy (NIRS) was proposed as a rapid and non-destructive technique to assess the content of three main phenolics (caffeic acid, (+)-catechin and chlorogenic acid) and three methylxanthines (caffeine, theobromine and theophylline) in SCG samples obtained from different coffee brands and diverse coffee machines. The content of these compounds was determined for 61 SCG samples by HPLC coupled with diode-array detection. Partial least squares (PLS) regression based models were calibrated to correlate diffuse reflectance NIR spectra against the reference data for the six parameters obtained by HPLC. Spectral wavelength selection and number of latent variables were optimized by minimizing the cross-validation error. PLS models showed good linearity with a coefficient of determination for the prediction set (Rp2) of 0.95, 0.92, 0.88, 071 and 0.84 for caffeine, caffeic acid, (+)-catechin, chlorogenic acid and theophylline, respectively. The range error ratio (RER) was higher for caffeine (17.8) when compared to other compounds (12.0, 10.1, 7.6 and 9.2, respectively for caffeic acid, (+)-catechin, chlorogenic acid and theophylline). Moreover, the content of caffeine could be used to predict the antioxidant properties of SCG samples (R=0.808, n=61), despite not presenting this property itself. The results obtained confirmed that NIRS is a suitable technique to screen SCG samples unveiling those with high content of bioactive compounds, which are interesting for subsequent extraction procedures.

A Feasibility Study on the Evaluation of Quality Properties of Chinese Rice Wine Using Raman Spectroscopy

Rapid analysis of Chinese rice wine (CRW) is an important activity for quality assurance and control investigations. In recent years, due to its insensitivity to water and fewer overlapped bands, Raman spectroscopy (RS) may provide more useful qualitative and quantitative information on functional groups of various chemical compounds in CRWs than the conventional spectroscopic technique (e.g., infrared spectroscopy); there has been a growing interest in the application of RS in the qualitative and quantitative analysis in food industry. In this study, the applicability of RS hyphenated with chemometrics using different pretreated spectra was examined to develop rapid, low-cost, and non-destructive method for quantification of four enological parameters involved in CRW quality control. Partial least square (PLS) was used for building the calibration models for the four chemical parameters based on the full RS spectrum. The model was also optimized by using efficient wavelength selection algorithm, i.e., synergy interval partial least square (SiPLS) algorithm. In addition, soft independent modeling of class analogy (SIMCA) and linear discriminant analysis (LDA) were used as classification techniques to predict the brands (wineries) of CRW samples. The results demonstrated that compared with the PLS model using all wavelengths of RS spectra, the prediction precision of model based on the spectral variables selected by SiPLS was significantly improved with high values of the coefficient of determination (>0.90), residual predictive deviation (>3.0), and range error ratio (>10) for all of the four quality parameters. The SIMCA and LDA results, characterized by high percentages of correct classification (96.67 and 100.00 % as average value in prediction for SIMCA and LDA, respectively), showed that samples belonging to a particular brand could be correctly classified. The overall results indicated the suitability of RS combined with efficient variable selection algorithm to rapidly control the quality of CRW.

Fabrication of a current transformer test set calibrator and its applications

A current transformer test set (CTTS) calibrator consisting of capacitors and resistors was fabricated. The calibrator was utilized for the calibration of several commercial CTTSs and covered a ratio error and phase error range up to ±10% and ±10 crad, respectively. From the calculated and measured values of the corresponding readings, the errors in the ratio and phase readings are derived. The calibration results for several commercial CTTSs are presented with the measurement uncertainty and are compared with the specifications provided by the manufacturer. The absolute uncertainty of a CTTS for both the ratio and the phase for the entire investigated range was obtained. The absolute uncertainty for the ratio error range of –0.1% to +0.05% was found to be less than 10 × 10−6 and the absolute uncertainty of the phase error range of –0.1 to +0.1 crad was not more than 6 µrad.

Estimation of moisture curves in cork granulate by Vis/NIRS technology

The aim of this study was to determine the moisture content of cork granulate between maximum moisture content and equilibrium moisture content and evaluate the potential of Vis/NIR spectroscopy in predicting the moisture content in that range. The study was carried out in a set comprising 118 samples classified into nine granulometric classes (0.5–1, 1–1.5, 1–2, 1.5–2.5, 2–4, 2–5, 2–6, 3–7, and 4–8 mm). Spectra were collected by reflectance in the wavelength range of 400–2500 nm. Approximately 400 spectra were used to develop modified partial least squares calibration equations, while 180 spectra were used for the external validation. The mean maximum moisture content was 661.2 % (range 967.5–271.3 %) after 80 days of saturation, while the mean equilibrium moisture content was 6.2 % (range 5.8–7.5 %) after 58 days of drying depending on the granulometric class. The calibrations achieved excellent accuracy and precision with coefficients of determination in the cross-validation $$ (r_{\text{cv}}^{2} ) $$ of 0.99. The best equation showed a coefficient of determination of external validation $$ (r_{\text{EV}}^{2} ) $$ of 0.99, similar standard errors of cross-validation (24.22 %) and external validation (26.96 %), a ratio of performance to deviation of 11.7, and a range error ratio of 39.7 in the external validation, thus confirming that Vis/NIRS technology is suitable for quantifying the moisture content of cork granulate in routine analyses.

Determination of Cellulose Crystallinity of Banana Residues Using Near Infrared Spectroscopy and Multivariate Analysis

Crystallinity is an important property of lignocellulosic biomass due to its significant effect on acid/enzymatic hydrolysis. Normally, physicochemical analysis, such as powder X-ray diffraction and nuclear magnetic resonance, is used to reveal the crystallinity content. However, these analytical methods are expensive and laborious. In this context, methods that rapidly predict the crystallinity are important, even if used only for screening calibration. Thus, we intend to show the potential of near-infrared spectroscopy (NIRS) and chemometrics to replace reference methods in crystallinity determination. The results show that NIRS can be used to determine crystallinity in banana residues by the use of partial least squares regression, providing good coefficients of determination (R2cal,pred > 0.82), low relative errors (< 14%) and good range error ratio (≥ 7.7). The interpretation of the regression coefficients, multivariate figures of merit and external validation results indicate a strong relationship between the NIR spectrum and crystallinity in banana samples.

Value Adding to Red Grape Pomace Exploiting Eco-friendly FT-NIR Spectroscopy Technique

Agro-food industries require sustainable and profitable alternatives to disposal of their by-products. Grape pomace is a winemaking residue that represents a low-cost natural source of phenolic compounds with recognized antioxidant properties. In this work, Fourier-transform near-infrared (FT-NIR) spectroscopy and chemometric analysis were exploited to the characterization of red grape pomace composition (content of seeds and skins) and chemical properties as total phenolic content (TPC) and total antioxidant capacity (TAC). Raw (n = 96) and milled (n = 96) samples were evaluated by NIR spectroscopy and by classical batchwise assays, Folin-Ciocalteu, and ABTS for TPC and TAC, respectively, after different storage times (1 week to 2 months). Grape seeds had higher levels of TPC and TAC per sample dry weight when compared to grape skins. FT-NIR spectra of raw and milled samples were calibrated against content (%) of skins and seeds, TPC, and TAC using partial least squares (PLS) modeling. Spectral wavelength selection and latent variables were optimized for the lowest root-mean-square errors. PLS models’ results showed higher linearity for milled samples (0.936 > R 2 > 0.914) when compared to raw samples (0.885 > R 2 > 0.928). The range error ratio (RER) was between 10 and 14 for raw samples, while for milled grape pomace, it ranged from 15 to 18. Results confirmed that NIR spectroscopy can be applied to winemaking residues with virtually no sample processing needed to estimate the content of grape seeds and skins, the total phenolics, and total antioxidant capacity. Therefore, FT-NIR technique represents a non-destructive and eco-friendly technique to foster added value of grape pomace residues before time-consuming extraction steps are performed.

Developing a PLS model for determination of total phenolic content in aged cachaças

The aging of cachaça in wooden casks is an important step in the production process of this beverage because in addition to the improvement of properties, it also increases its market value greatly. According to the Brazilian legislation in order to prove that this drink was aged, the presence of phenolic compounds must be detected and the most commonly method used for this is the Folin–Ciocalteu method which demands expenditure of time and reagents. Thus, this paper aims to propose a direct method for determination of total phenolic content using UV–Vis spectra of diluted aged cachaça and the multivariate calibration method PLS (partial least squares). Satisfactory results were obtained in the concentration range from 0.73 to 82.34mgGAEL−1 (mg-equivalent of gallic acid per liter) providing root mean square error of prediction of 4.11mgGAEL−1. The method was validated through the estimation of figures of merit such as trueness, precision, sensitivity, analytical sensitivity, bias, relative prediction deviation and range error ratio. Then the proposed method was considered adequate for the rapid determination of total phenolic content in aged cachaça and could be an alternative for routine analysis.

Selection for milk coagulation properties predicted by Fourier transform infrared spectroscopy in the Italian Holstein-Friesian breed

Milk coagulation is based on a series of physicochemical changes at the casein micelle level, resulting in formation of a gel. Milk coagulation properties (MCP) are relevant for cheese quality and yield, important factors for the dairy industry. They are also evaluated in herd bulk milk to reward or penalize producers of Protected Designation of Origin cheeses. The economic importance of improving MCP justifies the need to account for this trait in the selection process. A pilot study was carried out to determine the feasibility of including MCP in the selection schemes of the Italian Holstein. The MCP were predicted in 1,055 individual milk samples collected in 16 herds (66±24 cows per herd) located in Brescia province (northeastern Italy) by means of Fourier transform infrared (FTIR) spectroscopy. The coefficient of determination of prediction models indicated moderate predictions for milk rennet coagulation time (RCT=0.65) and curd firmness (a30=0.68), and poor predictions for curd-firming time (k20=0.49), whereas the range error ratio (8.9, 6.9, and 9.5 for RCT, k20, and a30, respectively) indicated good practical utility of the predictive models for all parameters. Milk proteins were genotyped and casein haplotypes (αS1-, β-, αS2-, and κ-casein) were reconstructed. Data from 51 half-sib families (19.9±16.4 daughters per sire) were analyzed by an animal model to estimate (1) the genetic parameters of predicted RCT, k20, and a30; (2) the breeding values for these predicted clotting variables; and (3) the effect of milk protein genotypes and casein haplotypes on predicted MCP (pMCP). This is the first study to estimate both genetic parameters and breeding values of pMCP, together with the effects of milk protein genotypes and casein haplotypes, that also considered k20, probably the most important parameter for the dairy industry (because it indicates the time for the beginning of curd-cutting). Heritability of predicted RCT (0.26) and k20 (0.31) were close to the average heritability described in literature, whereas the heritability of a30 was higher (0.52 vs. 0.27). The effects of milk proteins were statistically significant and similar to those obtained on measured MCP. In particular, haplotypes including uncommon variants showed positive (B-I-A-B) or negative (B-A1-A-E) effects. Based on these findings, FTIR spectroscopy-pMCP is proposed as a potential selection criterion for the Italian Holstein.

Prediction of hot-water-soluble extractive, pentosan and cellulose content of various wood species using FT-NIR spectroscopy

The potential of near infrared spectroscopy in conjunction with partial least squares regression to predict chemical composition of various wood species including softwoods and hardwoods was examined. Hot-water-soluble extractive, pentosan and cellulose content of various wood species were predicted with high coefficient of determination between the predicted and measured values, the ratio of performance to deviation, range error ratio, and low root mean square error of cross validation for cross-validation and root mean square error of prediction for test set validation. Hot-water-soluble extractive and cellulose content models were only suitable for quality control analysis, but pentosan content model had an excellent fit with the data and could be used in any application. All the results indicate that Fourier transform near infrared spectroscopy could be applied to predict the chemical composition of various wood species.

FT-NIR spectroscopy as a tool for valorization of spent coffee grounds: Application to assessment of antioxidant properties

Spent coffee grounds (SCGs) are a significant residue produced annually worldwide. This by-product contains high levels of bioactive compounds, such as chlorogenic acid and flavonoids that have recognized antioxidant properties. Recovery of these compounds previously for utilization in energy production is an interesting valorization approach, since they can be further incorporated in food products. A rapid, clean and reliable technique is required to discriminate SCG samples with high content of total phenolics, and total flavonoids as well as to assess their antioxidant capacity. In this work, Fourier-transform near infrared (FT-NIR) spectroscopy was used to determine the antioxidant capacity, total phenolic and total flavonoid contents of several SCG samples (n=101). The antioxidant capacity of SCGs was determined by the ABTS assay, using the direct procedure that measures both soluble and insoluble antioxidant fractions and by the indirect approach that assesses the antioxidants present in SCG ethanolic extracts. FT-NIR spectra were calibrated against total flavonoid and total phenolic contents and the antioxidant capacity using PLS modeling. Spectral wavelength selection and latent variables were optimized for the lowest root mean square errors. All PLS models' results showed good linearity with an R2 for an independent test set of 0.93, 0.96, 0.95 and 0.95 for antioxidant capacity of SCGs, antioxidant capacity of SCG ethanolic extracts, and total flavonoid and total phenolic contents, respectively. The range error ratio (RER) was higher than 13 for all models (higher than 16 for three of them). Results confirmed that NIR spectroscopy is a promising technique for routine assessment of these parameters in SCGs and is a viable and advantageous alternative to chemical procedures involving laborious extractions.

Enhancing the Use of Argos Satellite Data for Home Range and Long Distance Migration Studies of Marine Animals

Accurately quantifying animals’ spatial utilisation is critical for conservation, but has long remained an elusive goal due to technological impediments. The Argos telemetry system has been extensively used to remotely track marine animals, however location estimates are characterised by substantial spatial error. State-space models (SSM) constitute a robust statistical approach to refine Argos tracking data by accounting for observation errors and stochasticity in animal movement. Despite their wide use in ecology, few studies have thoroughly quantified the error associated with SSM predicted locations and no research has assessed their validity for describing animal movement behaviour. We compared home ranges and migratory pathways of seven hawksbill sea turtles (Eretmochelys imbricata) estimated from (a) highly accurate Fastloc GPS data and (b) locations computed using common Argos data analytical approaches. Argos 68th percentile error was <1 km for LC 1, 2, and 3 while markedly less accurate (>4 km) for LC ≤0. Argos error structure was highly longitudinally skewed and was, for all LC, adequately modelled by a Student’s t distribution. Both habitat use and migration routes were best recreated using SSM locations post-processed by re-adding good Argos positions (LC 1, 2 and 3) and filtering terrestrial points (mean distance to migratory tracks ± SD = 2.2±2.4 km; mean home range overlap and error ratio = 92.2% and 285.6 respectively). This parsimonious and objective statistical procedure however still markedly overestimated true home range sizes, especially for animals exhibiting restricted movements. Post-processing SSM locations nonetheless constitutes the best analytical technique for remotely sensed Argos tracking data and we therefore recommend using this approach to rework historical Argos datasets for better estimation of animal spatial utilisation for research and evidence-based conservation purposes.

Development and evaluation of models for specific gravity of Eucalyptus tereticornis wood by fourier transformed near infrared spectroscopy and partial least squares regression analysis

Specific gravity is 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 accessory for fast prediction of specific gravity of dry timber (moisture content 12 %) of Eucalyptus tereticornis from varying age group. Specific gravity was determined by conventional method and correlated with near infrared spectra using partial least square regression. The coefficient of determination of cross validation (R cv 2 ) is in the range of 0.80–0.91 for radial face and 0.88–0.94 for tangential face. The calibration equations when applied to test set resulted in coefficient of determination (R TS 2 ) ranging from 0.68 to 0.93 for radial face and 0.90 to 0.94 for tangential face. The ratio performance of deviation was calculated as the ratio of the standard deviation of the prediction set to the root mean square error of prediction and it was found to be between 2.1 and 3.8 for radial face and 3.2–4.1 for tangential face. The values were found identical when groups i.e., calibration and test set were interchanged. The range error ratio for both faces models were also identical and indicating that models are quite satisfactory for screening purpose.