Potential effectiveness of visible and near infrared spectroscopy coupled with wavelength selection for real time grapevine leaf water status measurement

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.

Simultaneous sex and species classification of silkworm pupae by NIR spectroscopy combined with chemometric analysis

BACKGROUND

Most studies only focus on the sex discrimination of silkworm pupae. However, species differentiation of silkworm pupae is also needed in sericulture. To classify the sex and species at the same time, the present study adopts near infrared (NIR) spectroscopy combined with multivariate analysis.

RESULTS

First, spectra samples were acquired using an NIR sensor, comprising female and male silkworm pupae from three species. Second, three different variables selection approaches were used, including a successive projections algorithm, competitive adaptive reweighted sampling (CARS) and interval partial least squares (iPLS). Third, identification models were built based on random forest and partial least squares discriminant analysis (PLSDA). The experimental results show that iPLS-PLSDA model (95.24%) gives a high performance when using the one of the three variable selection methods alone. To further increase the performance, the variable selection methods are optimized. The accuracy of the iPLS-CARS-PLSDA model is as high as 98.41%.

CONCLUSION

The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for sex and species identification of silkworm pupae. © 2020 Society of Chemical Industry.

Prediction of starch reserves in intact and ground grapevine cane wood tissues using near-infrared reflectance spectroscopy

BACKGROUND

Near-infrared reflectance spectroscopy (NIRS) technology can be a powerful analytical technique for the assessment of plant starch, but generally samples need to be freeze-dried and ground. This study investigated the feasibility of using NIRS technology to quantify starch concentration in ground and intact grapevine cane wood samples (with or without the bark layer). A partial least squares regression was used on the sample spectral data and was compared against starch analysis using a conventional wet chemistry method.

RESULTS

Accurate calibration models were obtained for the ground cane wood samples (n = 220), one based on 17 factors (R² = 0.88, root mean square error of validation (RMSEV) of 0.73 mg g^-1 ) and the other based on 10 factors (R² = 0.85, RMSEV of 0.80 mg g^-1 ). In contrast, the prediction of starch within intact cane wood samples was very low (R² = 0.19). Removal of the cane bark tissues did not substantially improve the accuracy of the model (R² = 0.34). Despite these poor correlations and low ratio of prediction to deviation values of 1.08-1.24, the root mean square error of cross-validation (RMSECV) values were 0.75-0.86 mg g^-1 , indicating good predictability of the model.

CONCLUSIONS

As indicated by low RMSECV values, NIRS technology has the potential to monitor grapevine starch reserves in intact cane wood samples. © 2020 Society of Chemical Industry.

Determination of 10-Hydroxy-2-Decenoic Acid of Royal Jelly Using Near-Infrared Spectroscopy Combined with Chemometrics

A rapid quantitative analysis model for determining the hydroxy-2-decenoic acid (10-HDA) content of royal jelly based on near-infrared spectroscopy combining with PLS has been developed. Firstly, near-infrared spectra of 232 royal jelly samples with different 10-HDA concentrations (0.35% to 2.44%) were be collected. Second-order derivative processing of the spectra was carried out to construct a full-spectrum PLS model. Secondly, GA-PLS, CARS-PLS, and Si-PLS were used to select characteristic wavelengths from the second-order derivative spectrum to construct a PLS calibration model. Finally, 58 samples were used to select the best predictive model for 10-HDA content. The result show that the PLS model constructed after wavelength selection was significantly more accurate than the full spectrum model. The Si-PLS algorithm performed best and the corresponding characteristic wavelength range were: 980 to 1038, 1220 to 1278, 1340 to 1398, and 1688 to 1746 nm. The prediction results were RMSEP = 0.1496% and R_P = 0.9380. Hence, it is feasible to employ near-infrared spectra to analyze 10-HDA in royal jelly.

FR4-based electromagnetic scanning micro-grating integrated with an angle sensor for a low-cost NIR micro-spectrometer

Aiming to implement a low-cost single-photodetector-based NIR micro-spectrometer, we present a novel flame retardant 4 (FR4)-driven micro-grating for spectral dispersion and spatial scanning. It consists of a silicon blazed grating directly bonded onto the FR4 actuator platform and an integrated differential electromagnetic angle sensor. Owing to the better shock and vibration reliability, larger aperture, shorter fabrication cycle, and much lower cost, it may be a potential alternative to conventional silicon microelectromechanical systems scanning micro-gratings. The micro-spectrometer prototype based on this device shows a spectral range of 800-1800 nm, a spectral accuracy of ±1.3  nm, and a 10 nm spectral resolution.