Standard Error Of Cross-validation Research Articles

Comparative near Infrared (NIR) spectroscopy calibrations performance of dried and undried forage on dry and wet matter bases

The application of Near Infrared (NIR) spectroscopy for analyzing wet feed directly on farms is increasingly recognized for its role in supporting harvest-time decisions and refining the precision of animal feeding practices. This study aims to evaluate the accuracy of NIR spectroscopy calibrations for both undried, unprocessed samples and dried, ground samples. Additionally, it investigates the influence of the bases of reference data (wet vs. dry basis) on the predictive capabilities of the NIR analysis. The study utilized 492 Corn Whole Plant (CWP) and 405 High Moisture Corn (HMC) samples, sourced from various farms across Italy. Spectral data were acquired from both undried, unground and dried, ground samples using laboratory bench NIR instruments, covering a spectral range of 1100 to 2498 nm. The reference chemical composition of these samples was analyzed and presented in two formats: on a wet matter basis and on a dry matter basis.The study revealed that calibrations based on undried samples generally exhibited lower predictive accuracy for most traits, with the exception of Dry Matter (DM). Notably, the decline in predictive performance was more pronounced in highly moist products like CWP, where the average error increased by 60–70%. Conversely, this reduction in accuracy was relatively contained (10–15%) in drier samples such as HMC. The Standard Error of Cross-Validation (SECV) values for DMres, Ash, CP, and EE were notably low, at 0.39, 0.30, 0.29, 0.21% for CWP and 0.49, 0.14, 0.25, 0.14% for HMC, respectively. These results align with previous studies, indicating the reliability of NIR spectroscopy in diverse moisture contexts.The study attributes this variance to the interference caused by water in 'as is' samples, where the spectral features predominantly reflect water content, thereby obscuring the spectral signatures of other nutrients. In terms of calibration development strategies, the study concludes that there is no significant difference in predictive performance between undried calibrations based on either 'dry matter' or 'as is' basis. This finding emphasizes the potential of NIR spectroscopy in diverse moisture contexts, although with varying degrees of accuracy contingent upon the moisture content of the analyzed samples. Overall, this research provides valuable insights into the calibration strategies of NIR spectroscopy and its practical applications in agricultural settings, particularly for on-farm forage analysis.

Predicting oil content of Australian beauty leaf tree kernel samples using near infrared spectroscopy combined with chemometrics

This study was conducted to evaluate the ability of near infrared (NIR) spectroscopy to estimate oil content, and per cent of cake, resin and residue in beauty leaf tree ( Calophyllum inophyllum L.) kernel samples. Fruits were collected from various geographical locations of tropical Australia (from Rockhampton to Darwin) and air dried before the kernels were manually separated from the fruits. Kernel samples were oven dried, crushed (5–10 mm) and their NIR spectra collected using a Fourier transform (FT) NIR instrument where the same batch of kernels were used to extract oil using a screw press. Calibration models between the NIR spectra and reference data were developed using partial least squares (PLS) regression. The cross-validation statistics including the coefficient of determination (r2) and standard error in cross validation (SECV) were 0.83 (SECV: 2.39%) for oil content, 0.89 (SECV: 2.81%) for cake, 0.88 (SECV: 1.92%) for resin and 0.79 (SECV: 2.15%) for residue, respectively. This research showed that NIR spectroscopy can be used as an alternative, faster and low-cost technique to predict oil content, per cent of cake, resins and residues in various genotypes of beauty leaf tree. Further studies should be carried out to increase the sample size and chemical variation, as well as to evaluate different methods of oil extraction (e.g., solvent extraction) to improve the reliability of the calibration models.

Predicting Egg Storage Time with a Portable Near-Infrared Instrument: Effects of Temperature and Production System.

Determining egg freshness is critical for ensuring food safety and security and as such, different methods have been evaluated and implemented to accurately measure and predict it. In this study, a portable near-infrared (NIR) instrument combined with chemometrics was used to monitor and predict the storage time of eggs under two storage conditions-room temperature (RT) and cold (CT) storage-from two production systems: cage and free-range. A total of 700 egg samples were analyzed, using principal component analysis (PCA) and partial least squares (PLS) regression to analyze the NIR spectra. The PCA score plot did not show any clear separation between egg samples from the two production systems; however, some egg samples were grouped according to storage conditions. The cross-validation statistics for predicting storage time were as follows: for cage and RT eggs, the coefficient of determination in cross validation (R2CV) was 0.67, with a standard error in cross-validation (SECV) of 7.64 days and residual predictive deviation (RPD) of 1.8; for CT cage eggs, R2CV of 0.84, SECV of 5.38 days and RPD of 3.2; for CT free-range eggs, R2CV of 0.83, SECV of 5.52 days and RPD of 3.2; and for RT free-range eggs, R2CV of 0.82, SECV of 5.61 days, and RPD of 3.0. This study demonstrated that NIR spectroscopy can predict storage time non-destructively in intact egg samples. Even though the results of the present study are promising, further research is still needed to further extend these results to other production systems, as well as to explore the potential of this technique to predict other egg quality parameters associated with freshness.

Pour point prediction of biodiesel-ethanol blends by near-infrared spectroscopy

Pour Point measurement is essential to assess biodiesel use in cold weather. In this work, biodiesel samples were obtained from 4 different types of cooking oil (sunflower oil, sunflower and soya oil, sunflower and corn oil, and corn oil). Biodiesel mixtures with ethanol were prepared by weight (% w/w). The pour point of the mixtures was measured according to the ASTM D97-08 procedure, which needs a cooling bath, able to attain very low temperatures (till −16 °C in the present work), and it is based on visual inspection of the mixture flowing ability. However, this procedure is tedious and time-consuming.NIR spectra of the biodiesel-ethanol mixtures were obtained in a very short time (approximately 1 min), and partial least-squares regression (PLS) was used to create a calibration model for the pour point prediction. In addition, the same spectra were used to develop a model for the ethanol content. Both models showed good prediction capability, with cross-validation standard error of prediction of 0.6177 for the percentage of ethanol and 0.9899 for the pour point. Although the error of prediction was higher for pour point than for ethanol, residual prediction deviation (RDP) for pour point was 3.4, and for ethanol it was 27, the lower RPD may be considered an excellent value for most analytical purposes.

Near infrared spectroscopy combined with chemometrics as tool to monitor starch hydrolysis

The objective of this study was to evaluate the feasibility of using near infrared (NIR) spectroscopy combined with principal component analysis (PCA) and partial least squares (PLS) regression to monitor the in vitro hydrolysis of different starch substrates. Potato and rice starches, and pre-gelatinised corn starch were used, where samples collected at different time points (5 to 120 min) during the in vitro hydrolysis and analysed using a Fourier transform NIR instrument with a gold-coated integrating sphere (diffuse reflection). PLS regression models between the spectra and reference data yield a coefficient of determination in cross validation (R2CV) and standard error in cross validation (SECV) of 0.94 and 1105. 8 μg mL−1; 0.81 and 440.81 μg mL−1; 0.45 and 338 μg mL−1; 0.70 and 276 μg mL−1; 0.75 and 296. 2 μg mL−1 for the prediction of the concentration of maltose using all samples, rice and potato combined, and pre-gelatinised corn, potato and rice starches analysed separately, respectively. It was concluded that the combination of NIR spectroscopy with both PCA and PLS regression might provide with a rapid and efficient tool to rapidly monitor changes that occur during the in vitro hydrolysis of starch.

Prediction model of the hardness of waterlogged archaeological wood based on NIR spectroscopy

The significance of waterlogged archaeological wood (WAW) lies in its profound informational value, encompassing historical, cultural, artistic, and scientific aspects of human civilization, and therefore need to be properly studied and preserved. In this study, the utilization of near-infrared (NIR) spectroscopy is employed as a predictive tool for assessing the hardness value of WAW. Given the submerged burial conditions, waterlogged wooden heritage frequently undergo substantial degradation in their physical and mechanical properties. The mechanical properties of waterlogged wooden heritage are essential for evaluating their state of preservation and devising appropriate conservation and restoration strategies. However, conventional methods for testing mechanical properties are limited by factors such as the availability of adequate sample size and quantity, adherence to the “principle of minimum intervention,” and cost considerations. NIR spectroscopy is a non-destructive, rapid, sensitive, and low-cost analytical technique with great potential for application in this area. In this study, two large and significant ancient Chinese shipwrecks were investigated. One hundred ninety-seven samples were collected and analyzed using NIR spectroscopy and a portable C-type shore hardness testing method. A partial least squares (PLS) regression model was developed to predict the hardness of the WAW. The model was optimized and validated using different preprocessing methods and spectral ranges. The results indicate that the best models were obtained with first derivatives + multiple scattering corrections (MSC) and first derivatives + standard normal variate (SNV) preprocessing in the 1000–2100 nm spectral range, both with an R2c of 0.97, a root mean squared error of correction (RMSEC) of 2.39 and 2.40, and a standard error of correction (SEC) of 2.40 and 2.41. Furthermore, they exhibited an R2v of 0.89 and 0.87, a root mean squared error of cross-validation (RMSECV) of 4.43 and 4.67, a standard error of cross-validation (SECV) of 4.45 and 4.68, and RPD values of 3.02 and 2.88, respectively. A coefficient of determination of the established prediction model (R2p) of 0.89 with a relative standard deviation for prediction (RSD) of 6.9% < 10% was obtained using a sample from the prediction set to predict the established model inversely. These results demonstrate that NIR spectroscopy could enable a rapid, non-destructive, and accurate estimation of the hardness of WAW. Moreover, by carefully choosing appropriate preprocessing techniques and spectral ranges, the predictive capabilities and accuracy of the model can be further enhanced. This research also contributes to the development of a theoretical framework and a methodological approach for future studies in this field. Furthermore, the data obtained from this study are crucial for determining effective preservation strategies for waterlogged archaeological wood.

Quantification of Dry Matter Content in Hass Avocado by Near-Infrared Spectroscopy (NIRS) Scanning Different Fruit Zones.

Accurate dry matter determination (DM) in Hass avocados is vital for optimal harvesting and ensuring fruit quality. Predictive models based on NIRS need to capture fruit DM gradient. This work aimed to determine the DM content in Hass avocado whole by NIRS scanning different fruit zones. Spectra were recorded for each zone of the fruit: peduncle (P), equator (E), and base (B). The calibration and validation included fruit from different orchards in two harvest cycles. The results show a DM gradient within the fruit: 24.47% (E), 24.68% (B), and 24.79% (P). The DM gradient was observed within the spectra using the RMSi (root mean square) criterion and PCA. The results show that at least one spectrum per fruit zone was needed to represent the variability within the fruit. The performances of the calibration using the whole set of data were R2: 0.74 and standard error of cross-validation (SECV) = 1.18%. In the validation stage using independent validation sets, the models showed similar performance (R2: 0.75, SECV 1.15%) with low values of the standard error of prediction (SEP): 1.62%. These results demonstrate the potential of near-infrared spectroscopy for high-throughput sorting of avocados based on their commercial quality.

Towards the automation of NIR spectroscopy to assess vineyard water status spatial–temporal variability from a ground moving vehicle

Irrigation has a strong impact in terms of yield regulation and grape and wine quality, so the implementation of precision watering systems would facilitate the decision-making process about the water use efficiency and the irrigation scheduling in viticulture. The objectives of this work were two-fold. On one hand, to compare and assess grapevine water status using two different spectral devices assembled in a mobile platform and to evaluate their capability to map the spatial variability of the plant water status in two commercial vineyards from July to early October in season 2021, and secondly to develop an algorithm capable of automate the spectral acquisition process using one of the two spectral sensors previously tested. Contemporarily to the spectral measurements collected from the ground vehicle at solar noon, stem water potential (Ψs) was used as the reference method to evaluate the grapevine water status. Calibration and prediction models for grapevine water status assessment were performed using the Partial least squares (PLS) regression and the Variable Importance in the Projection (VIP) method. The best regression models returned a determination coefficient for cross validation (R2cv) and external validation (R2p) of 0.70 and 0.75 respectively, and the standard error of cross validation (RMSECV) values were lower than 0.105 MPa and 0.128 MPa for Tempranillo and Graciano varieties using a more expensive and heavier near-infrared (NIR) spectrometer (spectral range 1200–2100 nm). Remarkable models were also built with the miniaturized, low-cost spectral sensor (operating between 900–1860 nm) ranging from 0.69 to 0.71 for R2cv, around 0.74 in both varieties for R2p and the RMSECV values were below 0.157 MPa, while the RMSEP values did not exceed 0.151 MPa in both commercial vineyards. This work also includes the development of a software which automates data acquisition and allows faster (up to 40% of time saving in the field) and more efficient deployment of the developed algorithm. The encouraging results presented in this work demonstrate the great potential of this methodology to assess the water status of the vineyard and estimate its spatial variability in different commercial vineyards, providing useful information for better irrigation scheduling.

Near Infrared Spectroscopy for Prediction of Yeast and Mould Counts in Black Soldier Fly Larvae, Feed and Frass: A Proof of Concept.

The use of black soldier fly larvae (BSFL) grown on different organic waste streams as a source of feed ingredient is becoming very popular in several regions across the globe. However, information about the easy-to-use methods to monitor the safety of BSFL is a major step limiting the commercialization of this source of protein. This study investigated the ability of near infrared (NIR) spectroscopy combined with chemometrics to predict yeast and mould counts (YMC) in the feed, larvae, and the residual frass. Partial least squares (PLS) regression was employed to predict the YMC in the feed, frass, and BSFL samples analyzed using NIR spectroscopy. The coefficient of determination in cross validation (R2CV) and the standard error in cross validation (SECV) obtained for the prediction of YMC for feed were (R2cv: 0.98 and SECV: 0.20), frass (R2cv: 0.81 and SECV: 0.90), larvae (R2cv: 0.91 and SECV: 0.27), and the combined set (R2cv: 0.74 and SECV: 0.82). However, the standard error of prediction (SEP) was considered moderate (range from 0.45 to 1.03). This study suggested that NIR spectroscopy could be utilized in commercial BSFL production facilities to monitor YMC in the feed and assist in the selection of suitable processing methods and control systems for either feed or larvae quality control.

Using mid‐infrared spectroscopy combined with chemometrics to determine digested starch and maltose concentration during in‐vitro digestion of starches

SummaryStarch digestion is typically evaluated using in‐vitro techniques. During and after the in‐vitro digestion, samples are collected (gastric and intestinal phases) at allotted time intervals where starch digestion is quantified through the analysis of sugars using routine chemical and enzymatic analysis. This paper reports the use of mid‐infrared (MIR) spectroscopy as rapid method to measure the concentration of maltose and % digested starch during the digestion of rice and potato starch, maize starch namely Gelose 50 (50% amylose), Gelose 80 (80% amylose) and pregelatinized corn starch collected at different time points (5, 10, 15, 20, 25, 30, 40, 50, 60, 75, 90, 105 and 120 min) from two in‐vitro digestion experiments. Two types of samples, turbid and supernatant were also evaluated. The coefficient of determination (R2) and the standard error in cross‐validation (SECV) ranged between 0.74 to 0.93 and 163 to 360 μg mL−1 for the prediction of the concentration of maltose. The use of turbid samples showed a better predictive ability when compared with the regression models developed using the supernatant samples. This method showed potential to significantly decrease the time and effort associated to determining maltose concentration and % digested starch during in‐vitro digestion.

Discrimination of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius) and beef (Bos taurus) binary mixtures using a portable near infrared instrument combined with chemometrics.

Consumers demand safe and nutritious foods at accessible prices; where issues associated with adulteration, fraud, and provenance have become important aspects to be considered by the modern food industry. There are many analytical techniques and methods available to determine food composition and quality, including food security. Among them, vibrational spectroscopy techniques are at the first line of defence (near and mid infrared spectroscopy, and Raman spectroscopy). In this study, a portable near infrared (NIR) instrument was evaluated to identify different levels of adulteration between binary mixtures of exotic and traditional meat species. Fresh meat cuts of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius) and beef (Bos taurus) sourced from a commercial abattoir were used to make different binary mixtures (95% %w/w, 90% %w/w, 50% %w/w, 10% %w/w and 5% %w/w) and analysed using a portable NIR instrument. The NIR spectra of the meat mixtures was analysed using principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). Two isosbestic points corresponding to absorbances at 1028nm and 1224nm were found to be consistent across all the binary mixtures analysed. The coefficient of determination in cross validation (R2) obtained for the determination of the per cent of species in a binary mixture was above 90% with a standard error in cross validation (SECV) ranging between 12.6 and 15%w/w. Overall, the results of this study indicate that NIR spectroscopy can determine the level or ratio of adulteration in the binary mixtures of minced meat.

Prediction of Anthocyanidins Content in Purple Chinese Cabbage Based on Visible/Near Infrared Spectroscopy.

Purple Chinese cabbage (PCC) has become a new breeding trend due to its attractive color and high nutritional quality since it contains abundant anthocyanidins. With the aim of rapid evaluation of PCC anthocyanidins contents and screening of breeding materials, a fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR). The PCC samples were scanned by NIR, and the spectral data combined with the chemometric results of anthocyanidins contents obtained by high-performance liquid chromatography were processed to establish the prediction models. The content of cyanidin varied from 93.5 mg/kg to 12,802.4 mg/kg in PCC, while the other anthocyanidins were much lower. The developed NIR prediction models on the basis of partial least square regression with the preprocessing of no-scattering mode and the first-order derivative showed the best prediction performance: for cyanidin, the external correlation coefficient (RSQ) and standard error of cross-validation (SECV) of the calibration set were 0.965 and 693.004, respectively; for total anthocyanidins, the RSQ and SECV of the calibration set were 0.966 and 685.994, respectively. The established models were effective, and this NIR method, with the advantages of timesaving and convenience, could be applied in purple vegetable breeding practice.

Modelling of Catechin Extraction from Red Grape Solids under Conditions That Simulate Red Wine Fermentation

Digital control systems are well established in many industries and could find application in the wine sector. Of critical importance to red wine quality, the efficient and targeted extraction of polyphenols from red grape solids during alcoholic fermentation could be a focus for automation. Smart technologies such as model predictive control (MPC) or fuzzy logic appear ideal for application in a complex process such as wine polyphenol extraction, but require mathematical models that accurately describe the system. The aim of this study was to derive and validate a model describing the extraction of catechin (a representative polyphenol) from red grape solids under simulated fermentation conditions. The impact of ethanol, fermentable sugar, and temperature on extraction rate was determined, with factor conditions chosen to emulate those present in industry practice. A first-order approach was used to generate an extraction model based on mass conservation that incorporated temperature and sugar dependency. Coefficients of determination (R2) for all test scenarios exceeded 0.94, indicating a good fit to the experimental data. Sensitivity analysis for the extraction rate and internal cross-validation showed the model to be robust, with a small standard error in cross-validation (SECV) of 0.11 and a high residual predictive deviation (RPD) of 17.68. The model that was developed is well suited to digital technologies where low computational overheads are desirable, and industrial application scenarios are presented for future implementation of the work.

Screening for pesticide residues in cocoa (Theobroma cacao L.) by portable infrared spectroscopy

Rapid assessment of pesticide residues ensures cocoa bean quality and marketability. In this study, a portable FTIR instrument equipped with a triple reflection attenuated total reflectance (ATR) accessory was used to screen cocoa beans for pesticide residues. Cocoa beans (n = 75) were obtained from major cocoa growing regions of Peru and were quantified for pesticides by gas chromatography (GC) or liquid chromatography (LC) coupled with mass spectrometry (MS). The FTIR spectra were used to detect the presence of pesticides in cocoa beans or lipid fraction (butter) by using a pattern recognition (Soft Independent Modeling by Class Analogy, SIMCA) algorithm, which produced a significant discrimination for cocoa nibs (free or with pesticides). The variables related to the class grouping were assigned to the aliphatic (3200–2800 cm−1) region with an interclass distance (ICD) of 3.3. Subsequently, the concentration of pesticides in cocoa beans was predicted using a partial least squares regression analysis (PLSR), using an internal validation of the PLRS model, the cross-validation correlation coefficient (Rval = 0.954) and the cross-validation standard error (SECV = 14.9 mg/kg) were obtained. Additionally, an external validation was performed, obtaining the prediction correlation coefficient (Rpre = 0.940) and the standard error of prediction (SEP = 16.0 μg/kg) with high statistical performances, which demonstrates the excellent predictability of the PLSR model in a similar real application. The developed FTIR method presented limits of detection and quantification (LOD = 9.8 μg/kg; LOQ = 23.1 μg/kg) with four optimum factors (PC). Mid-infrared spectroscopy (MIR) offered a viable alternative for field screening of cocoa.

Discrimination of dyslipidemia types with ATR-FTIR spectroscopy and chemometrics associated with multivariate analysis of the lipid profile, anthropometric, and pro-inflammatory biomarkers

BackgroundObesity, dyslipidemia, and low-grade inflammatory state form a triad of self-sustaining metabolic dysfunction. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy is a simple, rapid, and non-destructive technique that generates spectral fingerprints of biomolecules that can be correlated with metabolic changes. We verified the efficiency of ATR-FTIR spectroscopy in blood plasma (n = 74) to discriminate the types of dyslipidemias and suggest metabolic inflammatory changes. MethodsPrincipal Component Analysis (PCA) was performed on the biochemical and anthropometric data to verify whether the dyslipidemia types share a similar biochemical profile plausible of discrimination in chemometric modeling. To discriminate the types of dyslipidemias based on spectral data, Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) was used and validated with leave-one-out cross-validation. ResultsAlthough no significant difference was obtained between the types of dyslipidemia and normal subjects by CRP, leptin, and cfDNA, there was a significant difference between normal subjects vs combined hyperlipidemia (CH) + hypercholesterolemia (HCL) + hypertriglyceridemia (HTG) (p < 0.05) by the 1245 cm−1 peak [νas(PO2-)] (possible indication of chronic inflammation by increased cfDNA). The area under the curve of the region between 1770 and 1720 cm−1 was significantly increased for CH in relation to other dyslipidemias and normal subjects. Furthermore, there were significant differences for the main representative peaks of lipids, proteins, carbohydrates, and nucleic acids between the types of dyslipidemias and between the types of dyslipidemias and normal subjects. The OPLS-DA model achieved 100 % accuracy with 1 latent variable and Standard Error of Cross-Validation (SECV) < 0.004 for all types of dyslipidemia and the control group. ConclusionsOur results suggest that ATR-FTIR spectroscopy associated with chemometric modeling is a plausible applicant for screening the types of dyslipidemias. However, more extensive studies should be conducted to verify the real applicability in clinical analysis laboratories or medical clinics.

Comparison of feed tables, empirical models and near-infrared spectroscopy to predict chemical composition and net energy of pelleted pig feeds

The determination of the net energy (NE) of a pig feed requires expensive in vivo experiments. Alternatives are available whether the feed ingredient composition is known or unknown. We compared three estimation methods: feed tables, empirical models based on chemical parameters and in vitro digestibility and near-infrared spectroscopy (NIRS) calibrations based on feed and feces spectra. All methods were evaluated for their accuracy in predicting chemical composition with a focus on NE, the applicability in practice, and practical limitations. From a total dataset of 80 feeds and 480 feces samples originating from 3 in vivo digestibility trials, 28 feeds were used to examine the use of feed tables, 62 feeds for empirical models and 62 feeds and 310 feces samples for NIRS calibrations. Feed tables accurately predicted most chemical components (R² > 0.90), except for crude ash, sugar, acid detergent lignin and moisture. The dry matter (DM) content was underestimated on average by 16 g/kg and 17 g/kg from Dutch (CVB) and French (INRA) tables, respectively. The direct use of tabular NE values overestimated feeds containing beet pulp. The prediction of NE, calculated according to the CVB system, from the tabular chemical composition and digestibility coefficients of the feed ingredients resulted in a standard error of estimate (SEE) of 0.29 and 0.43 MJ/kg for CVB and INRA tables, respectively. Expressing these values on DM basis decreased SEE to 0.22 and 0.30 MJ/kg DM for CVB and INRA, respectively. The best empirical model to predict NE based on chemical composition resulted in a standard error of cross-validation (SECV) of 0.21 MJ/kg. Incorporating the in vitro digestibility of organic matter allowed to decrease SECV to 0.18 MJ/kg. A NIRS calibration based on feed spectra alone resulted in a SECV of 0.33 MJ/kg, whereas the combination of feed and feces spectra decreased SECV to 0.26 MJ/kg. All three estimation methods have advantages and disadvantages. Feed tables allow a quick estimation of the composition and average NE content, but they can only be used for feeds with known and common ingredient composition. Empirical models allow the most accurate estimate of the NE of feeds of unknown composition, but they require expensive and time-consuming analyses. Finally, a NIRS calibration based on feed and feces spectra enables a fast estimation of NE, but is less precise than empirical models and requires sample preparation and a large dataset.

Predicting fecal composition, intake, and nutrient digestibility in beef cattle consuming high forage diets using near infrared spectroscopy.

The objective of this study was to develop near infrared spectroscopy (NIRS) calibrations to predict fecal nutrient composition, intake, and diet digestibility from beef cattle fed high forage diets. Heifers were fed 12 different forage-based diets (>95% forage dry matter basis) in 3 total collection digestibility studies, resulting in individual fecal samples and related spectra (n = 135), corresponding nutrient intake, and apparent total tract digestibility (aTTD) data. Fecal samples were also collected from steers grazing two annual and two perennial forage mixtures over two growing seasons. Samples (n = 13/paddock) were composited by paddock resulting in 30 samples from year 1, and 24 from year 2. The grazing fecal spectra (n = 54) were added to the existing fecal composition spectral library. Dried and ground fecal samples were scanned using a FOSS DS2500 scanning monochromator (FOSS, Eden Prairie, MN). Spectra were mathematically treated for detrend and scatter correction and modified partial least squares (MPLS) regression was performed. The coefficient of determination for cross validation (R2cv) and standard error of cross validation (SECV) were used to evaluate the quality of calibrations. Prediction equations were developed for fecal composition [organic matter (OM), nitrogen (N), amylase-treated ash-corrected neutral detergent fiber (aNDFom), acid detergent fiber (ADF), acid detergent lignin (ADL), undigestible NDF after 240 h of in vitro incubation (uNDF), calcium (Ca), and phosphorus (P)], digestibility [DM, OM, aNDFom, N], and intake [DM, OM, aNDFom, N, uNDF]. The calibrations for fecal OM, N, aNDFom, ADF, ADL, uNDF, Ca, P resulted in R2cv between 0.86 and 0.97 and SECV of 1.88, 0.07, 1.70, 1.10, 0.61, 2.00, 0.18, and 0.06, respectively. Equations predicting intake of DM, OM, N, aNDFom, ADL, and uNDF resulted in R2cv values between 0.59 and 0.91, SECV values of 1.12, 1.10, 0.02, 0.69, 0.06, 0.24 kg·d-1, respectively, and SECV values between 0.00 and 0.16 when expressed as % body weight (BW). Digestibility calibrations for DM, OM, aNDFom, and N resulted in R2cv ranging from 0.65 to 0.74 and SECV values from 2.20 to 2.82. We confirm the potential of NIRS to predict fecal chemical composition, digestibility, and intake of cattle fed high forage diets. Future steps include validation of the intake calibration equations for grazing cattle using forage internal marker and modelling energetics of grazing growth performance.

A predictive model for the determination of cadmium concentration in cocoa beans using laser-induced plasma spectroscopy

This study proposes a predictive model to determine the concentration of cadmium (Cd) in cocoa beans based on laser-induced breakdown spectroscopy (LIBS) and partial least squares regression (PLSR-1 or PLS-1). The multivariate calibration model was developed using 46 cocoa bean samples, with Cd concentrations up to 1 mg kg-1. The increase of the LIBS signal in the Cd emission lines was evident when the cocoa bean sample was subjected to a solid-liquid-solid transformation (SLST). The range error ratio (RER) was 7.92, which allowed it to be classified as a screening model. Monte Carlo cross-validation was used, with 60% of samples for calibration and the remaining for testing. The standard error of cross-validation (SECV) and standard error of calibration (SEC) were 0.12 mg kg-1 and 0.05 mg kg-1, respectively. The proposed procedure is framed within the alternatives for the chemical analysis of cocoa.

Application of miniaturized near-infrared spectrometer for indirectly determining flesh thickness of intact polished coconut

Consumers prefer polished coconut with a certain flesh thickness that is associated with maturity. At present, sorting mature polished coconuts is conducted manually. A portable near-infrared spectrometer in the range 900–1700 nm was adopted to determine the flesh thickness non-destructively. Averaged near-infrared spectra scanned in diffuse reflectance mode in three sections of the coconut bottom were used to create the predictive model using partial least squares regression. Samples from two harvests of the polished coconut were used. The first harvest samples were used to build and validate the model, and the second harvest samples were used to test the model performance independently. The final validated model, based on the samples from two harvests, provided acceptable performance, with values for the correlation coefficient, standard error of cross-validation, and bias of 0.89, 0.68 mm, and − 0.006 mm, respectively. The near-infrared absorbance contained only shell spectral information that was confirmed by testing the penetration depth. The highest loading weight of the best model corresponded with the lignin absorption peak.

Towards the non-invasive assessment of staling in bovine hides with hyperspectral imaging

Microbial spoilage or staling of bovine hides during storage leads to poor leather quality and increased chemical consumption during processing. Conventional microbiological examinations of hide samples which require time-consuming microbe culture cannot be employed as a practical staling detection approach for leather production. Hyperspectral imaging (HSI), featuring fast data acquisition and implementation flexibility has been considered ideal for in-line detection of microbial contamination in Agri- food products. In this study, a linescan hyperspectral imaging system working in a spectral range of 550 nm to 1700 nm was utilized as a rapid and non-destructive technique for predicting the aerobic plate counts (APC) on raw hide samples during storage. Fresh bovine hide samples were stored at 4 °C and 20 °C for 3 days. Every day, hyperspectral images were acquired on both sides for each sample. The APCs were determined simultaneously by conventional microbiological plating method. Leather quality was evaluated by microscopic inspection of grain surfaces, which indicate the acceptable threshold of microbe load on hide samples for leather processing. Partial least squares regression (PLSR) was applied to fit the spectral information extracted from the samples to the logarithmic values of APC to develop microbe load prediction models. All models showed good prediction accuracy, yielding a Rcv2 in the range of 0.74–0.92 and standard error of cross validation (SECV) in the range of 0.61–0.76 %. The prediction capability of the HSI was explored using the model developed with SNV + smoothened pre-processing to spatially predict plate count in the samples. Models established in this study successfully predicted the staling states characterised by bacterial loads on hide samples with low prediction errors. Models, visually, showed the differences in microbial load across the storage time and temperatures. Results illustrate that HSI can be potentially implemented as a non-invasive tool to predict microbe loads in bovine hides before leather processing, so that real-time grading of hides based on staling states can be achieved. This will reduce the cost of leather production and waste management and pave the way for allocating material supply for different production purposes.