Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato.

Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQexternal), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQexternal values of 0.937, 0.892, and 0.759 were obtained for protein, dry matter, and total phenols, respectively. Higher RPD values were found for protein (3.982), followed by dry matter (3.041) and total phenolics (2.000), which indicates the excellent predictability of the models. A paired t-test confirmed that the differences between laboratory and predicted values are non-significant. This study presents the first multi-trait NIRS prediction model for Indian potato germplasm. The developed NIRS model effectively predicted the remaining genotypes in this study, demonstrating its broad applicability. This work highlights the rapid screening potential of NIRS for potato germplasm, a valuable tool for identifying trait variations and refining breeding strategies, to ensure sustainable potato production in the face of climate change.

Near-infrared reflectance spectroscopy for rapid prediction of biochemical methane potential of wastewater wasted sludge.

The information of biochemical methane potential (BMP) of wasted sludge is essential to ensure the stable operation of sludge management processes. However, conventional anaerobic digestion (AD) approach for BMP test is time-consuming and labour-intensive. Currently, the technique of Near Infrared Spectroscopy (NIRS) is gaining prominence in the biogas production within AD process. Previous studies mostly focused on predicting BMP values for fibrous plant biomass and solid waste, with only a limited number of studies attempting to apply NIRS to obtain BMP values across a wide array of wasted sludge types. To obtain BMP values for this diverse range of wasted sludge efficiently and accurately, it is imperative to develop precise models for assessing BMP values using NIRS. In this study, the possibility of using NIRS to predict the BMP values of wasted sludge was evaluated. A total of 70 sludge samples from different sources were investigated to develop a BMP-prediction model by correlating the measured BMP values with the obtained NIR spectra. As a result, a reliable and successful BMP-prediction model was established with the determination coefficient of 0.90, residual prediction deviation of 3.50 and low root mean square error of prediction of 36.8 mL CH4/g VS. This BMP-prediction model is satisfactory for predicting BMP values of various types of sludge. It could provide support for plant operators to make decisions rapidly, thereby improving the process efficiency and optimizing sludge management procedures.

Detection of winter tick, Dermacentor albipictus, infestations using near infrared reflectance spectroscopy of bovine feces.

The objective of this study was to determine whether artificial infestations of D. albipictus could be detected in cattle using near infrared reflectance spectroscopy of bovine feces (fNIRS) and if detection capability was sensitive to size of tick infestation and phase of on-host stage-specific tick development. Fecal samples were collected daily from six non-infested then later tick-infested Bos taurus yearling heifers who each served as their own control. Cattle with D. albipictus infestations arising from as few as 1000 larvae were identified by fecal chemistry changes using fNIRS technology. In two separate trials, three animal pairs were infested with one of three treatment levels (low: ∼ 1000, medium: ∼ 4000, and high: ∼ 8000) of D. albipictus larvae in a repeated measures experimental design. Trial 1 consisted of tick naïve cattle while Trial 2 consisted of prior tick exposed cattle. Date of drop and daily sum of engorged female ticks were tabulated to characterize each infestation. Cluster, common factor, principal component and MANOVA analyses were used to define and assess fecal spectra changes associated with experimental stages of infestation. Cluster analyses found significant differences in fecal samples for heifer pairs in each treatment level group (low, medium, and high) in Trial 1 and then in Trial 2 from two pre-infestation control periods (outside and inside), three stages of tick development (larval feeding, nymphal feeding, adult feeding), and post-tick recovery periods. Five shifts in fecal chemistry of non-infested and tick-infested periods were identified by six clusters of NIRS fecal spectra measured between 576 and 1126 nm. The PCA's resulted in 97.56% and 97.77% for Trials 1 and 2 respectively of the total variation in the 1050 frequencies being explained by the first three principal components (P1, P2, P3). Results from the MANOVA and the Wilk's Lambda test for both trials showed highly significant evidence (p-values < 0.0001) of a difference in the means of the three principal components across the six Stages. There was significant evidence in Trial 1 (p-values = 0.0067) and Trial 2 (p-values < 0.0001) of a difference between the means of the three principal components across the three levels of tick infestation. These significant pair-wise comparisons reflect developmental phases of tick attachment and blood-feeding that define periods of increasing, peak and declining stress identified in five fecal chemistry shifts defined by six fecal spectral clusters.

The use of novel diffuse optical spectroscopies for improved neuromonitoring during neonatal cardiac surgery requiring antegrade cerebral perfusion.

Background: Surgical procedures involving the aortic arch present unique challenges to maintaining cerebral perfusion, and optimal neuroprotective strategies to prevent neurological injury during such high-risk procedures are not completely understood. The use of antegrade cerebral perfusion (ACP) has gained favor as a neuroprotective strategy over deep hypothermic circulatory arrest (DHCA) due to the ability to selectively perfuse the brain. Despite this theoretical advantage over DHCA, there has not been conclusive evidence that ACP is superior to DHCA. One potential reason for this is the incomplete understanding of ideal ACP flow rates to prevent both ischemia from underflowing and hyperemia and cerebral edema from overflowing. Critically, there are no continuous, noninvasive measurements of cerebral blood flow (CBF) and cerebral oxygenation (StO2) to guide ACP flow rates and help develop standard clinical practices. The purpose of this study is to demonstrate the feasibility of using noninvasive, diffuse optical spectroscopy measurements of CBF and cerebral oxygenation during the conduct of ACP in human neonates undergoing the Norwood procedure. Methods: Four neonates prenatally diagnosed with hypoplastic left heart syndrome (HLHS) or a similar variant underwent the Norwood procedure with continuous intraoperative monitoring of CBF and cerebral oxygen saturation (StO2) using two non-invasive optical techniques, namely diffuse correlation spectroscopy (DCS) and frequency-domain diffuse optical spectroscopy (FD-DOS). Changes in CBF and StO2 due to ACP were calculated by comparing these parameters during a stable 5 min period of ACP to the last 5 min of full-body CPB immediately prior to ACP initiation. Flow rates for ACP were left to the discretion of the surgeon and ranged from 30 to 50 ml/kg/min, and all subjects were cooled to 18°C prior to initiation of ACP. Results: During ACP, the continuous optical monitoring demonstrated a median (IQR) percent change in CBF of -43.4% (38.6) and a median (IQR) absolute change in StO2 of -3.6% (12.3) compared to a baseline period during full-body cardiopulmonary bypass (CPB). The four subjects demonstrated varying responses in StO2 due to ACP. ACP flow rates of 30 and 40 ml/kg/min (n = 3) were associated with decreased CBF during ACP compared to full-body CPB. Conversely, one subject with a higher flow6Di rate of 50 ml/kg/min demonstrated increased CBF and StO2 during ACP. Conclusions: This feasibility study demonstrates that novel diffuse optical technologies can be utilized for improved neuromonitoring in neonates undergoing cardiac surgery where ACP is utilized. Future studies are needed to correlate these findings with neurological outcomes to inform best practices during ACP in these high-risk neonates.

Soil carbon content prediction using multi-source data feature fusion of deep learning based on spectral and hyperspectral images.

Visible near-infrared reflectance spectroscopy (VNIR) and hyperspectral images (HSI) have their respective advantages in soil carbon content prediction, and the effective fusion of VNIR and HSI is of great significance for improving the prediction accuracy. But the contribution difference analysis of multiple features in the multi-source data is inadequate, and there is a lack of in-depth research on the contribution difference analysis of artificial feature and deep learning feature. In order to solve the problem, soil carbon content prediction methods based on VNIR and HSI multi-source data feature fusion are proposed. The multi-source data fusion network under the attention mechanism and the multi-source data fusion network with artificial feature are designed. For the multi-source data fusion network based on the attention mechanism, the information are fused through the attention mechanism according to the contribution difference of each feature. For the other network, artificial feature are introduced to fuse multi-source data. The results show that multi-source data fusion network based on the attention mechanism can improve the prediction accuracy of soil carbon content, and multi-source data fusion network combined with artificial feature has better prediction effect. Compared with two single-source data from the VNIR and HSI, the relative percent deviation of Neilu, Aoshan Bay and Jiaozhou Bay based on multi-source data fusion network combined with artificial feature are increased by 56.81% and 149.18%, 24.28% and 43.96%, 31.16% and 28.73% respectively. This study can effectively solve the problem of the deep fusion of multiple features in the soil carbon content prediction by VNIR and HSI, so as to improve the accuracy and stability of soil carbon content prediction, promote the application and development of soil carbon content prediction in spectral and hyperspectral image, and provide technical support for the study of carbon cycle and the carbon sink.

Near-infrared reflectance spectroscopy accurately predicted isotope and elemental compositions for origin traceability of coffee.

Stable isotope ratios and trace elements are well-established tools that act as signatures of the product's environmental conditions and agricultural processes; but they involve time, money, and environmentally destructive chemicals. In this study, we tested for the first time the potential of near-infrared reflectance spectroscopy (NIR) to estimate/predict isotope and elemental compositions for the origin verification of coffee. Green coffee samples from two continents, 4 countries, and 10 regions were analysed for five isotope ratios (δ13C, δ15N, δ18O, δ2H, and δ34S) and 41 trace elements. NIR (1100-2400 nm) calibrations were developed using pre-processing with extended multiplicative scatter correction (EMSC) and mean centering and partial-least squares regression (PLS-R). Five elements (Mn, Mo, Rb, B, La) and three isotope ratios (δ13C, δ18O, δ2H) were moderately to well predicted by NIR (R2: 0.69 to 0.93). NIR indirectly measured these parameters by association with organic compounds in coffee. These parameters were related to altitude, temperature and rainfall differences across countries and regions and were previously found to be origin discriminators for coffee.

Analysis of the Composition of Different Instars of Tenebrio molitor Larvae using Near-Infrared Reflectance Spectroscopy for Prediction of Amino and Fatty Acid Content.

Insects are a sustainable protein source for food and feed. The yellow mealworm (Tenebrio molitor L.) is a promising candidate for industrial insect rearing and was the focus of this study. This research revealed the diversity of Tenebrio molitor larvae in the varying larval instars in terms of the nutritional content. We hypothesized that water and protein are highest in the earlier instar, while fat content is very low but increases with larval development. Consequently, an earlier instar would be a good choice for harvest, since proteins and amino acids content decrease with larval development. Near-infrared reflectance spectroscopy (NIRS) was represented in this research as a tool for predicting the amino and fatty acid composition of mealworm larvae. Samples were scanned with a near-infrared spectrometer using wavelengths from 1100 to 2100 nm. The calibration for the prediction was developed with modified partial least squares (PLS) as the regression method. The coefficient for determining calibration (R2C) and prediction (R2P) were >0.82 and >0.86, with RPD values of >2.20 for 10 amino acids, resulting in a high prediction accuracy. The PLS models for glutamic acid, leucine, lysine and valine have to be improved. The prediction of six fatty acids was also possible with the coefficient of the determination of calibration (R2C) and prediction (R2P) > 0.77 and >0.66 with RPD values > 1.73. Only the prediction accuracy of palmitic acid was very weak, which was probably due to the narrow variation range. NIRS could help insect producers to analyze the nutritional composition of Tenebrio molitor larvae fast and easily in order to improve the larval feeding and composition for industrial mass rearing.

Generation of Wheat Near-Isogenic Lines Overexpressing 1Bx7 Glutenin with Increased Protein Contents and SDS-Sedimentation Values.

Overexpression of Glu-1Bx7 via allele 1Bx7OE significantly contributes to high dough strength in some wheat varieties and is useful for improving wheat quality. However, the proportion of wheat varieties containing Bx7OE is quite low. In this study, four cultivars containing 1Bx7OE were selected, and among the selected varieties, Chisholm (1Ax2*, 1Bx7OE + 1By8*, and 1Dx5 + 1Dx10) was crossed with Keumkang, a wheat variety that contains 1Bx7 (1Ax2*, 1Bx7 + 1By8, and 1Dx5 + 1Dx10). SDS-PAGE and UPLC analyses showed that the expression of the high-molecular-weight glutenin subunit (HMW-GS) 1Bx7 was significantly higher in NILs (1Ax2*, 1Bx7OE + 1By8*, and 1Dx5 + 1Dx10) compared with that in Keumkang. Wheat quality was analyzed with near infrared reflectance spectroscopy by measuring the protein content and SDS-sedimentation of NILs. The protein content of NILs (12.94%) was 21.65% higher than that of Chisholm (10.63%) and 4.54% higher than that of Keumkang (12.37%). In addition, the SDS-sedimentation value of NILs (44.29 mL) was 14.97% and 16.44% higher than that of Keumkang (38.52 mL) and Chisholm (38.03 mL), respectively. This study predicts that the quality of domestic wheat can be improved by crossbreeding with 1Bx7OE-containing cultivars.

Identifying the "Dangshan" Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image.

The "Dangshan" pear woolliness response is a physiological disease that causes large losses for fruit farmers and nutrient inadequacies.The cause of this disease is predominantly a shortage of boron and calcium in the pear and water loss from the pear. This paper used the fusion of near-infrared Spectroscopy (NIRS) and Computer Vision Technology (CVS) to detect the woolliness response disease of "Dangshan" pears. This paper employs the merging of NIRS features and image features for the detection of "Dangshan" pear woolliness response disease. Near-infrared Spectroscopy (NIRS) reflects information on organic matter containing hydrogen groups and other components in various biochemical structures in the sample under test, and Computer Vision Technology (CVS) captures image information on the disease. This study compares the results of different fusion models. Compared with other strategies, the fusion model combining spectral features and image features had better performance. These fusion models have better model effects than single-feature models, and the effects of these models may vary according to different image depth features selected for fusion modeling. Therefore, the model results of fusion modeling using different image depth features are further compared. The results show that the deeper the depth model in this study, the better the fusion modeling effect of the extracted image features and spectral features. The combination of the MLP classification model and the Xception convolutional neural classification network fused with the NIR spectral features and image features extracted, respectively, was the best combination, with accuracy (0.972), precision (0.974), recall (0.972), and F1 (0.972) of this model being the highest compared to the other models. This article illustrates that the accuracy of the "Dangshan" pear woolliness response disease may be considerably enhanced using the fusion of near-infrared spectra and image-based neural network features. It also provides a theoretical basis for the nondestructive detection of several techniques of spectra and pictures.

Near-Infrared Reflectance Spectroscopy for Quantitative Analysis of Fat and Fatty Acid Content in Living Tenebrio molitor Larvae to Detect the Influence of Substrate on Larval Composition.

Several studies have shown that mealworms (Tenebrio molitor L.) could provide animals and humans with valuable nutrients. Tenebrio molitor larvae were studied to determine whether their rearing diets affected their fat and fatty acid content and to ascertain if it is possible to detect the changes in the larval fat composition using near-infrared reflectance spectroscopy (NIRS). For this reason, a standard control diet (100% wheat bran) and an experimental diet, consisting of wheat bran and the supplementation of a different substrate (coconut flour, flaxseed flour, pea protein flour, rose hip hulls, grape pomace, or hemp protein flour) were used. The results showed lesser weight gain and slower growth rates for larvae raised on diets with a high fat content. A total of eight fatty acids were identified and quantified, where palmitic, oleic, and linoleic acids were the most prevalent and showed a correlation between larval content and their content in the rearing diets. There was a high content of lauric acid (3.2-4.6%), myristic acid (11.4-12.9%), and α-linolenic acid 8.4-13.0%) in mealworm larvae as a result of the high dietary content of these fatty acids. NIR spectra were also influenced by the fat and fatty acid composition, as larval absorbance values differed greatly. The coefficient of the determination of prediction (R2P) was over 0.97, with an RPD value of 8.3 for the fat content, which indicates the high predictive accuracy of the NIR model. Furthermore, it was possible to develop calibration models with great predictive efficiency (R2P = 0.81-0.95, RPD = 2.6-5.6) for all fatty acids, except palmitoleic and stearic acids which had a low predictive power (R2P < 0.5, RPD < 2.0). The detection of fat and fatty acids using NIRS can help insect producers to quickly and easily analyze the nutritional composition of mealworm larvae during the rearing process.

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

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

An approach to detecting diphenylamine content and assessing chemical stability of single-base propellants by near-infrared reflectance spectroscopy.

Diphenylamine (DPA) as a stabilizer component plays an important role in maintaining the chemical stability of single-base propellants (SBPs). This work investigated the feasibility of rapidly detecting the content of DPA in SBP by near-infrared reflectance spectroscopy (NIRS). The quantitative NIR model was developed by intervals selection, spectral pretreatment and factor number optimization. The optimal spectral intervals were determined to be 1081 nm âˆ¼ 1280 nm and 1378 nm âˆ¼ 1602 nm based on the characteristic spectral peaks of DPA. By comparing the performance of the developed models with different preprocessing methods, the best preprocessing method was standard normal variate transformation (SNV) + de-trending (Dr) + Smoothing. The optimal number of factors was 6 for DPA model. Partial least squares (PLS) regression was used to establish the calibration models of DPA. For the developed model, the determination coefficients of calibration and prediction (Rc2, Rp2) were 0.9907 and 0.9884, respectively. The root mean square errors of calibration and prediction (RMSEC, RMSEP) were 0.0310 and 0.0342, respectively. The samples in the prediction set were predicted by the developed model, and the average absolute error of the proposed and reference method was only 0.0265. The developed model can be applied in rapid monitor the content of DPA in SBP. In addition, vieille test have demonstrated that the chemical stability of SBP became worse with the decrease of DPA content. The content of DPA contained in the SBP with qualified chemical stability is not less than 0.8753%. Thus, the developed model can be used to judge whether the chemical stability of SBP is qualified or unqualified.

Non-destructive determination of grass pea and pea flour adulteration in chickpea flour using near-infrared reflectance spectroscopy and chemometrics.

BACKGROUND: In order to obtain more economic gains, some food products are adulterated with low-cost substances, if they are toxic, they may pose public health risks. This has called forth the development of quick and non-destructive methods for detection of adulterants in food. Near-infrared reflectance spectroscopy (NIRS) has become a promising tool to detect adulteration in various commodities. We have developed rapid NIRS based analytical methods for quantification of two cheap adulterants (grass pea and pea flour) in a popular Indian food material, chickpea flour. RESULTS: The NIRS spectra of pure chickpea, pure grass pea, pure pea flour and adulterated samples of chickpea flour with grass pea and pea flour (1-90%) (w/w) were acquired and preprocessed. Calibration models were built based on modified partial least squares regression (MPLSR), partial least squares (PLS), principal component regression (PCR) methods. Based on lowest values of standard error of calibration (SEC) and standard error of cross-validation (SECV), MPLSR-NIRS models were selected. These models exhibited coefficient of determination (R2 ) of 0.999, 0.999, SEC of 0.905, 0.827 and SECV of 1.473, 1.491 for grass pea and pea, respectively. External validation revealed R2 and standard error of prediction (SEP) of 0.999 and 1.184, 0.997 and 1.893 for grass pea and pea flour, respectively. CONCLUSION: The statistics confirmed that our MPLSR-NIRS based methods are quite robust and applicable to detect grass pea and pea flour adulterants in chickpea flour samples and have potential for use in detecting food fraud. © 2022 Society of Chemical Industry.

Application of Near-Infrared Reflectance Spectroscopy for Predicting Chemical Composition of Feces in Holstein Dairy Cows and Calves.

Traditional methods for determining the chemical composition of cattle feces are uneconomical. In contrast, near-infrared reflectance spectroscopy (NIRS) has emerged as a successful technique for assessing chemical compositions. Therefore, in this study, the feasibility of NIRS in terms of predicting fecal chemical composition was explored. Cattle fecal samples were subjected to chemical analysis using conventional wet chemistry techniques and a NIRS spectrometer. The resulting fecal spectra were used to construct predictive equations to estimate the chemical composition of the feces in both cows and calves. The coefficients of determination for calibration (RSQ) were employed to evaluate the calibration of the predictive equations. Calibration results for cows (dry matter [DM], RSQ = 0.98; crude protein [CP], RSQ = 0.93; ether extract [EE], RSQ = 0.91; neutral detergent fiber [NDF], RSQ = 0.82; acid detergent fiber [ADF], RSQ = 0.89; ash, RSQ = 0.84) and calves (DM, RSQ = 0.92; CP, RSQ = 0.89; EE, RSQ = 0.77; NDF, RSQ = 0.76; ADF, RSQ = 0.92; ash, RSQ = 0.97) demonstrated that NIRS is a cost-effective and efficient alternative for assessing the chemical composition of dairy cattle feces. This provides a new method for rapidly predicting fecal chemical content in cows and calves.

Research on construction method and validity mechanism of robust analysis model in snow peach quality detection based on visible-near infrared spectroscopy.

Visible-near infrared (Vis-NIR) spectra analysis method is widely used in the quality grading of bulk fruits with its rapid, non-destructive, diverse detection modes and flexible modular integration scheme. However, during the online grading of fruits, the random mechanized way of dropping fruit onto the conveyor belt method and the open detection environment led to more spectral abnormal samples, which affect the accuracy of the detection. In this paper, the soluble solids content (SSC) of snow peach is quantitatively analyzed by static and online detection methods. Several spectral preprocessing methods including Norris-Williams Smoothing (NWS), Savitzky-Golay Smoothing (SGS), Continuous Wavelet Derivative (CWD), Multivariate Scattering Correction (MSC), and Variable Sorting for Normalization (VSN) are adopted to eliminate spectral rotation and translation errors and improve the signal-to-noise ratio. Monte Carlo Uninformative Variable Elimination (MCUVE) method is used for the selection of optimal characteristic modeling variables. Partial Least Squares Regression (PLSR) is used to model and analyze the preprocessed spectra and the spectral variables optimized by MCUVE, and the effectiveness of the method is evaluated. Sparse Partial Least Squares Regression (SPLSR) and Sparse Partial Robust M Regression (SPRMR) are used for the construction of robust models. The results showed that the SGS preprocessing method can effectively improve the analysis accuracy of static and online models. The MCUVE method can realize the extraction of stable characteristic variables. The SPRMR model based on SGS preprocessing method and the effective variables has the optimal analysis results. The analysis accuracy of snow peach static model is slightly better than that of online analytical model. Through the test results of the PLSR, SPLSR and SPRMR models by the artificially adding noise test method, it can be seen that the SPRMR method eliminates the influence of abnormal samples on the model during the modeling process, which can effectively improve the anti-noise ability and detection reliability.

Determination of Quality Parameters in Mangetout (Pisum sativum L. ssp. arvense) by Using Vis/Near-Infrared Reflectance Spectroscopy.

Pisum sativum L. ssp. arvense, is colloquially called tirabeque or mangetout because it is eaten whole; its pods are recognized as a delicatessen in cooking due to its crunch on the palate and high sweetness. Furthermore, this legume is an important source of protein and antioxidant compounds. Quality control in this species requires the analysis of a large number of samples using costly and laborious conventional methods. For this reason, a non-chemical and rapid technique as near-infrared reflectance spectroscopy (NIRS) was explored to determine its physicochemical quality (color, firmness, total soluble solids, pH, total polyphenols, ascorbic acid and protein content). Pod samples from different cultivars and grown under different fertigation treatments were added to the NIRS analysis to increase spectral and chemical variability in the calibration set. Modified partial least squares regression was used for obtaining the calibration models of these parameters. The coefficients of determination in the external validation ranged from 0.50 to 0.88. The RPD (standard deviation to standard error of prediction ratio) and RER (standard deviation to range) were variable for quality parameters and showed values that were characteristic of equations suitable for quantitative prediction and screening purposes, except for the total soluble solid calibration model.

Effects of Arterial Carbon Dioxide Tension on Cerebral and Somatic Regional Tissue Oxygenation and Blood Flow in Neonates After the Norwood Procedure With Deep Hypothermic Cardiopulmonary Bypass.

Neonates undergoing the Norwood procedure for hypoplastic left heart syndrome are at higher risk of impaired systemic oxygen delivery with resultant brain, kidney, and intestinal ischemic injury, shock, and death. Complex developmental, anatomic, and treatment-related influences on cerebral and renal-somatic circulations make individualized treatment strategies physiologically attractive. Monitoring cerebral and renal circulations with near infrared spectroscopy can help drive rational therapeutic interventions. The primary aim of this study was to describe the differential effects of carbon dioxide tension on cerebral and renal circulations in neonates after the Norwood procedure. Using a prospectively-maintained database of postoperative physiologic and hemodynamic parameters, we analyzed the relationship between postoperative arterial carbon dioxide tension and tissue oxygen saturation and arteriovenous saturation difference in cerebral and renal regions, applying univariate and multivariate multilevel mixed regression techniques. Results were available from 7,644 h of data in 178 patients. Increases in arterial carbon dioxide tension were associated with increased cerebral and decreased renal oxygen saturation. Differential changes in arteriovenous saturation difference explained these effects. The cerebral circulation showed more carbon dioxide sensitivity in the early postoperative period, while sensitivity in the renal circulation increased over time. Multivariate models supported the univariate findings and defined complex time-dependent interactions presented graphically. The cerebral and renal circulations may compete for blood flow with critical limitations of cardiac output. The cerebral and renal-somatic beds have different circulatory control mechanisms that can be manipulated to change the distribution of cardiac output by altering the arterial carbon dioxide tension. Monitoring cerebral and renal circulations with near infrared spectroscopy can provide rational physiologic targets for individualized treatment.

Bovine fecal chemistry changes with progression of Southern Cattle Tick, Rhipicephalus (Boophilus) microplus (Acari: Ixodidae) infestation.

Surveillance for cattle fever ticks is an essential activity in the U.S. Cattle Fever Tick Eradication Program which prevents reestablishment of these tick vectors of the pathogens causing bovine babesiosis. Other methods of detecting tick infested cattle could augment current physical inspection of restrained cattle by program inspectors. The objective of this study was to determine whether a single infestation of ∼5000 Rhipicephalus (Boophilus) microplus larvae induced changes in fecal chemistry that were detectable using near-infrared reflectance spectroscopy (NIRS). Fecal samples were collected daily from 6 tick-infested and 6 non-infested Bos taurus yearling heifers. Each infested animal received ticks from one of 6 different strains of laboratory colonies of R. microplus. Date of drop and daily sum of engorged female ticks were tabulated to characterize each infestation. Cluster, common factor, principal component and MANOVA analyses were used to define and assess fecal spectra changes associated with experimental stages of infestation. Cluster analyses found no significant differences in fecal samples from each of the 6 infested heifers. Two shifts in fecal chemistry of infested animals were identified by three clusters of NIRS fecal spectra. The first cluster was comprised of samples from pre-infestation to 9 days after infestation, a period inclusive of larval tick attachment and feeding. The second cluster was comprised of samples from day 10-22 corresponding to the period of nymphal feeding, adult feeding, and early drop of engorged females. A third cluster was comprised of samples from days 23-46 corresponding to the period of engorged female drop and declining tick numbers. A Tukey-Kramer multiple comparison procedure identified significant differences in fecal spectra between five experimental stages of R. microplus infestation for principal component 1 including pre-infestation to nymphal feeding, pre-infestation to adult feeding, larval feeding to adult feeding, nymphal feeding to adult feeding and nymphal feeding to engorged female drop; for principal component 2 including pre-infestation to nymphal feeding, pre-infestation to adult feeding, and pre-infestation to engorged female drop; and for principal component 3 including pre-infestation to drop, and adult feeding to drop. These significant pair-wise comparisons reflect developmental phases of tick attachment and blood-feeding that define periods of increasing, peak and declining stress identified in two fecal chemistry shifts defined by three fecal spectra clusters. Among non-infested animals, two shifts in fecal chemistry were also detected by three fecal-spectra clusters that occurred in synchrony with those of their tick-infested counterparts. There were no significant differences in principal components or MANOVA analyses between infested and non-infested animals and the pattern of significant pair-wise Tukey-Kramer multiple comparisons for non-infested animals were similar to those of infested animals. This unintended confounding effect is attributed to the manner in which all 12 animals were preconditioned as a group, then isolated in randomly assigned blind stalls in a common barn facility for the study, creating the basis for physiological stress resonance among non-infested animals.