This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400–1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides’ spectra of every seed), and mixture datasets (two sides’ spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner.

Background: Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data. Results: Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties. Conclusions: This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.

Seed priming is a method for improving seed vigor, which can improve seed germinability, but the disappearance of positive priming effects and decrease of seed longevity during storage may limit its application. To determine the effect of priming on hot pepper seeds and the time during which priming effects can be maintained, it is necessary to monitor and study the variability of primed seeds during storage. In this study, several priming treatments with 3% KNO₃, 20% PEG, or 3% KNO₃ + 20% PEG and up to 8 months of storage of primed seeds were conducted on seeds of pepper strains No.63, No.73, and No.101. Germinability was improved by priming treatment. The germination percentage of primed seeds did not vary compared to non-primed ones, seeds germinated quickly and had strengthened emergence, especially for those primed with 3% KNO₃ solution, which germinated significantly faster and the seedlings grew stronger. During the 8-month storage process, the beneficial effects of priming disappear after 6 months, beyond which the performance of primed seeds is worse than non-primed seeds. After 8 months of storage, the viability of primed pepper seeds dropped sharply. In general, preservation at −4 °C can retain the priming effects to the greatest degree and prevent the seed from losing viability for up to 6 months. Resistance to aging differed across varieties, with No.101 pepper seeds more resistant to aging than the other two varieties, regardless of priming treatment.

Near-infrared (NIR) spectroscopy technology has demonstrated great potential in the analysis of complex samples owing to its simplicity, rapidity and being nondestructive. In this investigation, we compare the abilities of six popular multivariate classification techniques, extreme learning machine (ELM), support vector machine (SVM), semi-supervised SVM (S³VM), twin support vector machine (TWSVM), regularized logistic regression (RLR) and minimax probability machine (MPM). Two datasets of near-infrared spectroscopy data are used for classification comparison and the 5000–10 000 cm⁻¹ NIR spectral region is chosen. When there are sufficient labeled data in the dataset, experimental results on different spectral regions illustrate that all six methods perform very well for identifying the hardness of licorice seeds, while the four methods, ELM, SVM, TWSVM and S³VM, are also very powerful for recognizing the purity of maize seeds. When there are relatively few labeled data, the S³VM can improve the generalization by incorporating unlabeled data in training for licorice seed classification. Compared with traditional linear discriminant analysis, the six proposed methods achieve better performances in two NIR datasets. These results show that these methods are feasible and effective in the analysis of near-infrared spectral data. And we hope that the results can help further investigations of chemometrics and NIR spectroscopy data.

Rapid volatile detection methods for seed vigour rely heavily on artificial ageing (AA), however the comparability of volatile organic compounds (VOCs) to natural ageing (NA) and practicability of the detection models were not well known. In this study, VOCs between AA and NA sweet corn seeds were compared and Partial Least Squares Regression (PLS-R) modelswere constructed based on AA to predict the seed vigour of NA. A total of 33 VOCs were identified, among which aldehydes showed the highest consistency between NA and AA. Furthermore, a AS-PLS-R model with variable importance in projection (VIP > 1) and Pearson Correlation Coefficient (r > 0.9) algorithms, which was built on 3 volatile markers: benzaldehyde monomer, n-nonanal, 1-butanol monomer, achieved the best performance (R²p of 0.901 and RMSEP of 0.050). Therefore, coupling Gas Chromatography- Ion Mobility Spectrometry (GC-IMS) with chemometrics can be an effective way to monitor and predict stored seeds vigour.