Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy

Abstract

The combination of hyperspectral imaging with multivariate data analysis methods has recently been applied to develop a nondestructive technique, required to determine the seed viability of artificially aged vegetable and cereal seeds. In this study, the potential of shortwave infrared hyperspectral imaging to determine the viability of naturally aged seeds was investigated and thereafter a model for online seed sorting system was developed. The hyperspectral images of 400 Hinoki cypress tree seeds were acquired, and germination tests were conducted for viability confirmation, which indicated 31.5% of the viable seeds. Partial least square discriminant analysis models with 179 variables in the wavelength region of 1000–1800 nm were developed with a maximum model accuracy of 98.4% and 93.8% in both the calibration and validation sets, respectively. The partial least square discriminant analysis beta coefficient revealed the key wavelengths to differentiate viable from nonviable seeds, determined based on the differences in the chemical compositions of the seeds, including their lipid and fatty acid contents, which may control the germination ability of the seeds. The most effective wavelengths were selected using two model-based variable selection methods (i.e., the variable importance of projection (15 variables) and the successive projections algorithm (8 variables)) to develop the model. The successive projections algorithm wavelength selection method was considered to develop a viability model, and its application to the raw data resulted in a prediction accuracy of 94.7% in the calibration set and 92.2% in the validation set. These results demonstrate the potential of shortwave infrared hyperspectral imaging spectroscopy as a powerful nondestructive method to determine the viability of Hinoki cypress seeds. This method could be applied to develop an online seed sorting system for seed companies and nurseries.

High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng (Panax ginseng Meyer) Using a Hyperspectral Reflectance Image

“…In this work, two laboratory-based line scan HSI systems in different wave ranges, Vis/NIR (400–1000 nm) [ 22 ] and SWIR (1000–1800 nm) [ 23 ], were used in reflectance mode to collect the hyperspectral images of plant samples ( Figure 1 b). The spectral intensity beyond 1800 nm was significantly low and noisy, compared to the range of shorter wavelength.…”

Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds

“…Additionally, it was employed because of its simplicity and high accuracy. The seeds were divided into calibration and validation sets [16,39,40]. A high accuracy was obtained using the method and is thus recommended for classification.…”

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method

“…One major advantage of hyperspectral imaging is its capability to create a chemical image of component distributions from simultaneous measurement of spectral and spatial data. The novel benefit of the chemical image is that it provides a measurement of different parameters of a chemical component, sample to sample, or even with the same sample, at each pixel location [ 40 , 41 , 42 , 43 ]. The PLSR beta coefficient was used to develop a chemical image of adulterated almond powder with mixed substances.…”