Brassica juncea depends heavily on nitrogen (N) fertilizers for growth and accumulation of seed protein. However, it is an inefficient mobilizer of applied N which leads to accumulation of excess N in the soil, posing environmental risks. Hence, it is imperative to systematically examine spatial-temporal pattern of crop N to efficiently manage N application. The Kjeldahl method is commonly used to estimate N status of crops but it is a destructive method that entails the use of perilous and expensive chemicals. Near-infrared reflectance spectroscopy (NIRS) offers a safe, accurate, and non-destructive alternative for large-scale screening of seed metabolites. Currently, no NIRS model exists to quickly estimate N content in shoots and roots from large germplasm sets in any rapeseed-mustard crop. Developing such a model is essential to breed for enhanced nitrogen use efficiency (NUE). We used 738 shoot and 346 root samples from a B. juncea diversity set to construct the NIRS models. A diverse range of genetic variation in N content was recorded in the stem (0.21-6.61%) and root (0.15-3.04%) tissues of the crop raised on two different N levels (N0 and N100). Modified partial least squares (MPLS) method was employed to establish a regression equation linking reference N values with spectral changes. The developed models exhibited strong associations with reference values, with RSQ values of 0.884 for stem and 0.645 for roots. Furthermore, external validation confirms the reliability of the developed models. The developed models have strong predictive capabilities for rapid and reliable N estimation in various tissues of B. juncea plants.

The development of calibration models using Raman spectra data has long been challenged owing to the substantial time and cost required for robust data acquisition. To reduce the number of experiments involving actual incubation, a calibration model development method was investigated by measuring artificially mixed samples. In this method, calibration datasets were prepared using spectra from artificially mixed samples with adjusted concentrations based on design of experiments. The precision of these calibration models was validated using the actual cell culture sample. The results showed that when the culture conditions were unchanged, the root mean square error of prediction (RMSEP) of glucose, lactate, and antibody concentrations was 0.34, 0.33, and 0.25 g/L, respectively. Even when variables such as cell line or culture media were changed, the RMSEPs of glucose, lactate, and antibody concentrations remained within acceptable limits, demonstrating the robustness of the calibration models with artificially mixed samples. To further improve accuracy, a model training method for small datasets was also investigated. The spectral pretreatment conditions were optimized using error heat maps based on the first batch of each cell culture condition and applied these settings to the second and third batches. The RMSEPs improved for glucose, lactate, and antibody concentration, with values of 0.44, 0.19, and 0.18 g/L under constant culture conditions, 0.37, 0.12, and 0.12 g/L for different cell lines, and 0.26, 0.40, and 0.12 g/L when the culture media was changed. These results indicated the efficacy of calibration modeling with artificially mixed samples for actual incubations under various conditions.

The flexible strain sensor\'s measuring range is usually over 5000 με, while the conventional variable section cantilever calibration model has a measuring range within 1000 με. In order to satisfy the calibration requirements of flexible strain sensors, a new measurement model was proposed to solve the inaccurate calculation problem of the theoretical strain value when the linear model of a variable section cantilever beam was applied to a large range. The nonlinear relationship between deflection and strain was established. The finite element analysis of a variable section cantilever beam with ANSYS shows that the linear model\'s relative deviation is as high as 6% at 5000 με, while the relative deviation of the nonlinear model is only 0.2%. The relative expansion uncertainty of the flexible resistance strain sensor is 0.365% (k = 2). Simulation and experimental results show that this method solves the imprecision of the theoretical model effectively and realizes the accurate calibration of a large range of strain sensors. The research results enrich the measurement models and calibration models for flexible strain sensors and contribute to the development of strain metering.

Virgin coconut oil (VCO) is a functional food with important health benefits. Its economic interest encourages fraudsters to deliberately adulterate VCO with cheap and low-quality vegetable oils for financial gain, causing health and safety problems for consumers. In this context, there is an urgent need for rapid, accurate, and precise analytical techniques to detect VCO adulteration. In this study, the use of Fourier transform infrared (FTIR) spectroscopy combined with multivariate curve resolution-alternating least squares (MCR-ALS) methodology was evaluated to verify the purity or adulteration of VCO with reference to low-cost commercial oils such as sunflower (SO), maize (MO) and peanut (PO) oils. A two-step analytical procedure was developed, where an initial control chart approach was designed to assess the purity of oil samples using the MCR-ALS score values calculated on a data set of pure and adulterated oils. The pre-treatment of the spectral data by derivatization with the Savitzky-Golay algorithm allowed to obtain the classification limits able to distinguish the pure samples with 100% of correct classifications in the external validation. In the next step, three calibration models were developed using MCR-ALS with correlation constraints for analysis of adulterated coconut oil samples in order to assess the blend composition. Different data pre-treatment strategies were tested to best extract the information contained in the sample fingerprints. The best results were achieved by derivative and standard normal variate procedures obtaining RMSEP and RE% values in the ranges of 1.79-2.66 and 6.48-8.35%, respectively. The models were optimized using a genetic algorithm (GA) to select the most important variables and the final models in the external validations gave satisfactory results in quantifying adulterants, with absolute errors and RMSEP of less than 4.6% and 1.470, respectively.

Digestibility and intake are parameters difficult and expensive to estimate under grazing conditions; therefore, the aim of this study was to develop near-infrared reflectance spectroscopy (NIRS) calibrations applied to feces (F-NIRS) and evaluate their accuracy to predict dry matter digestibility (DMD) and dry matter intake (DMI) of Colombian creole cattle. Five digestibility trials using creole steers were conducted; indigestible neutral detergent fiber (iNDF) was used as internal marker and Cr2O3 and TiO2 as external markers. A total of 249 forage and 396 fecal samples from individual animals were collected, dried, and grinded for conventional chemical analysis. For spectral analysis, fecal samples were pooled across collection periods (77 samples). Chemometric analysis was performed using WinISI V4.10 software applying the modified partial least squares method. Cross-validation was performed to avoid overfitting the models. The goodness-of-fit statistics considered were the coefficient of determination in cross-validation and prediction sets (R2cv and r2, respectively) and the ratio performance deviation (RPD). Fecal NIRS calibrations developed for forage and supplement DMD showed a satisfactory fit (R2cv =0.87 and RPD=2.77 and R2cv=0.92 and RPD=3.50, respectively). The accuracy of fecal output equations using chromium (Cr) and titanium (Ti) was similar in terms of R2cv (0.92) and RPD (3.63 vs. 3.57). Total DMI equations using Ti performed better compared to Cr (R2cv = 0.82 vs. 0.78; RPD=2.41 vs. 2.17, respectively). The F-NIRS models were validated using a completely independent set of fecal samples showing a moderate fit (r2>0.8 and RPD>2.0). This study showed that F-NIRS is a feasible tool to predict DMD and DMI of creole steers under grazing conditions. However, previous to socialization, this requires an improvement in accuracy of the calibrated equations related to grazing animals in different production contexts.

Strain sensors, especially fiber Bragg grating (FBG) sensors, are of great importance in structural health monitoring, mechanical property analysis, and so on. Their metrological accuracy is typically evaluated by equal strength beams. The traditional strain calibration model using the equal strength beams was built based on an approximation method by small deformation theory. However, its measurement accuracy would be decreased while the beams are under the large deformation condition or under high temperature environments. For this reason, an optimized strain calibration model is developed for equal strength beams based on the deflection method. By combining the structural parameters of a specific equal strength beam and finite element analysis method, a correction coefficient is introduced into the traditional model, and an accurate application-oriented optimization formula is obtained for specific projects. The determination method of optimal deflection measurement position is also presented to further improve the strain calibration accuracy by error analysis of the deflection measurement system. Strain calibration experiments of the equal strength beam were carried out, and the error introduced by the calibration device can be reduced from 10 με to less than 1 με. Experimental results show that the optimized strain calibration model and the optimum deflection measurement position can be employed successfully under large deformation conditions, and the deformation measurement accuracy is improved greatly. This study is helpful to effectively establish metrological traceability for strain sensors and furthermore improve the measurement accuracy of strain sensors in practical engineering scenarious.

Snow pear is very popular in southwest China thanks to its fruit texture and potential medicinal value. Lignin content (LC) plays a direct and negative role (higher concentration and larger size of stone cells lead to thicker pulp and deterioration of the taste) in determining the fruit texture of snow pears as well as consumer purchasing decisions of fresh pears. In this study, we assessed the robustness of a calibration model for predicting LC in different batches of snow pears using a portable near-infrared (NIR) spectrometer, with the range of 1033-2300 nm. The average NIR spectra at nine different measurement positions of snow pear samples purchased at four different periods (batch A, B, C and D) were collected. We developed a standard normal variate transformation (SNV)-genetic algorithm (GA) -the partial least square regression (PLSR) model (master model A) - to predict LC in batch A of snow pear samples based on 80 selected effective wavelengths, with a higher correlation coefficient of prediction set (Rp) of 0.854 and a lower root mean square error of prediction set (RMSEP) of 0.624, which we used as the prediction model to detect LC in three other batches of snow pear samples. The performance of detecting the LC of batch B, C, and D samples by the master model A directly was poor, with lower Rp and higher RMSEP. The independent semi-supervision free parameter model enhancement (SS-FPME) method and the sequential SS-FPME method were used and compared to update master model A to predict the LC of snow pears. For the batch B samples, the predictive ability of the updated model (Ind-model AB) was improved, with an Rp of 0.837 and an RMSEP of 0.614. For the batch C samples, the performance of the Seq-model ABC was improved greatly, with an Rp of 0.952 and an RMSEP of 0.383. For the batch D samples, the performance of the Seq-model ABCD was also improved, with an Rp of 0.831 and an RMSEP of 0.309. Therefore, the updated model based on supervision and learning of new batch samples by the sequential SS-FPME method could improve the robustness and migration ability of the model used to detect the LC of snow pears and provide technical support for the development and practical application of portable detection device.

The accuracy and precision of laser-induced breakdown spectroscopy (LIBS) quantitative analysis are significantly limited by the spectral noise. Normalization and ensemble averaging of multiple spectra were often used to preprocess spectra. However, these methods cannot completely remove the spectral noise. Data uncertainty due to the irremovable spectral noise will affect LIBS quantitative analysis. Therefore, this paper proposes a method using data uncertainty to improve the performance of LIBS quantitative analysis. The proposed method uses several spectra to characterize each sample to preserve some data uncertainty in the calibration data matrix. Thus, the data uncertainty is used to optimize the calibration model for improving the toleration to the spectral signal variation. As a result, the optimized calibration model had better accuracy and robustness than the calibration model trained by conventional method. The best root mean square error of prediction (RMSEP) of the ash content of coal was 1.152% for the optimized calibration model, while that for the conventional calibration model was 1.718%. The optimized calibration model also showed a lower relative standard deviation (RSD) value of repeated predictions. Moreover, the calibration model for predicting the ash content in biomass was also optimized by the proposed method. The optimized calibration model outperformed the conventional calibration model again, which demonstrated the extensive applicability of the proposed method.

The importance of poly-unsaturated fatty acids (PUFAs) in food is crucial for the animal and human development and health. As a complementary strategy to nutrition approaches, genetic selection has been suggested to improve fatty acids (FAs) composition in farmed fish. Gas chromatography (GC) is used as a reference method for the quantification of FAs; nevertheless, the high cost prevents large scale phenotyping as needed in breeding programs. Therefore, a calibration by means of Raman scattering spectrometry has been established in order to predict FA composition of visceral adipose tissue in rainbow trout Onchorhynchus mykiss. FA composition was analyzed by both GC and Raman micro-spectrometry techniques on 268 individuals fed with three different feeds, which have different FA compositions. Among the possible regression methods, the ridge regression method, was found to be efficient to establish calibration models from the GC and spectral data. The best cross-validated R2 values were obtained for total PUFAs, omega-6 (Ω-6) and omega-3 (Ω-3) PUFA (0.79, 0.83 and 0.66, respectively). For individual Ω-3 PUFAs, α-linolenic acid (ALA, C18:3), eicosapentaenoic acid (EPA, C20:5) and docosahexenoic acid (DHA, C22:6) were found to have the best R2 values (0.82, 0.76 and 0.81, respectively). This study demonstrates that Raman spectroscopy could be used to predict PUFAs with good correlation coefficients on adipocytes, for future on adipocytes physiology or for large scale and high throughput phenotyping in rainbow trout.

Spectra data of 300 samples from 6 Cucurbitaceae commodities, including zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber, were recorded using a handheld visible/near-infrared (Vis/NIR) instrument. Vis/NIR data were obtained in the form of absorbance spectra data at a wavelength of 381-1065 nm. The spectral data has the potential to be reused to predict quality attributes in the form of soluble solids and water content on several Cucurbitaceae commodities. The accuracy of the Vis/NIR calibration model can be increased by applying spectra preprocessing, for example, second derivative savitzky-golay (dg2). The calibration model was developed using the principal component regression (PCR) method on RAW and dg2 spectra. The enhanced Vis/NIR dataset can be used to evaluate the inner quality attributes of intact fruits in a rapid, non-destructive manner.