The determination of strawberry fruit quality through the traditional destructive lab techniques has some limitations related to the amplitude of the samples, the timing and the applicability along all phases of the supply chain. The aim of this study was to determine the main qualitative characteristics through traditional lab destructive techniques and Near Infrared Spectroscopy (NIR) in fruits of five strawberry genotypes. Principal Component Analysis (PCA) was applied to search for spectral differences among all the collected samples. A Partial Least Squares regression (PLS) technique was computed in order to predict the quality parameters of interest. The PLS model for the soluble solids content prediction was the best performing—in fact, it is a robust and reliable model and the validation values suggested possibilities for its use in quality applications. A suitable PLS model is also obtained for the firmness prediction—the validation values tend to worsen slightly but can still be accepted in screening applications. NIR spectroscopy represents an important alternative to destructive techniques, using the infrared region of the electromagnetic spectrum to investigate in a non-destructive way the chemical–physical properties of the samples, finding remarkable applications in the agro-food market.

Scattering effect is a really common physical phenomenon during near‐infrared analysis. It is an undesired variation in the spectral data due to a deviation of light from a straight trajectory into different paths. The nonlinearities introduced can be handled by using spectral preprocessing techniques. The situation is completely different when the parameter of interest is physical by nature, such as ash content, in this case removing the physical artifacts of scattering would be negative for the final model. In this study, we have decided to investigate the ash content parameter trying to figure out if the information useful for its prediction is related to the scattering effects, the chemical features, or a mixture of them. To this aim, two near‐infrared spectral datasets were taken into consideration: woodchip for energy sector and pellet samples for feed sector. A new regression model (CORR‐PLS) was developed by including principal components analysis scores and extended multiplicative scatter correction (EMSC) factors as physical parameters into the partial least squares (PLS) regression model. The prediction performance of the regular PLS models (PLS on the raw data and MSC pre‐treated data) were compared with that of the CORR‐PLS model both with regard to prediction uncertainty and model complexity in order to evaluate which is the relevant information for prediction of the ash content.

Recycling of post-consumer waste wood material is becoming an increasingly appealing alternative to disposal. However, its huge heterogeneity is calling for an assessment of the material characteristics in order to define the best recycling option and intended reuse. In fact, waste wood comes into a variety of uses/types of wood, along with several levels of contamination, and it can be divided into different categories based on its composition and quality grade. This study provides the measurement of more than a hundred waste wood samples and their characterisation using a hand-held NIR spectrophotometer. Three classification methods, i.e. K-nearest Neighbours (KNN), Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA) and PCA-KNN, have been compared to develop models for the sorting of waste wood in quality categories according to the best-suited reuse. In addition, the classification performance has been investigated as a function of the number of the spectral measurements of the sample and as the average of the spectral measurements. The results showed that PCA-KNN performs better than the other classification methods, especially when the material is ground to 5 cm of particle size and the spectral measurements are averaged across replicates (classification accuracy: 90.9 %). NIR spectroscopy, coupled with chemometrics, turned out to be a promising tool for the real-time sorting of waste wood material, ensuring a more accurate and sustainable waste wood management. Obtaining real-time information about the quality and characteristics of waste wood material translates into a decision of the best recycling option, increasing its recycling potential.

Strawberry is the most cultivated berry fruit globally and it is really appreciated by consumers because of its characteristics, mainly bioactive compounds with antioxidant properties. During the breeding process, it is important to assess the quality characteristics of the fruits for a better selection of the material, but the conventional approaches involve long and destructive lab techniques. Near infrared spectroscopy (NIR) could be considered a valid alternative for speeding up the breeding process and is not destructive. In this study, a total of 216 strawberry fruits belonging to four different cultivars have been collected and analyzed with conventional lab analysis and NIR spectroscopy. In detail, soluble solid content, acidity, vitamin C, anthocyanin, and phenolic acid have been determined. Partial least squares discriminant analysis (PLS-DA) models have been developed to classify strawberry fruits belonging to the four genotypes according to their quality and nutritional properties. NIR spectroscopy could be considered a valid non-destructive phenotyping method for monitoring the nutritional parameters of the fruit and ensuring the fruit quality, speeding up the breeding program.