Assessment of macadamia kernel quality defects by means of near infrared spectroscopy (NIRS) and nuclear magnetic resonance (NMR)

Macadamia kernels are visually sorted based on the presence of quality defects by specialized labors. However, this process is not as accurate as non-destructive methods such as near infrared spectroscopy (NIRS) and nuclear magnetic resonance (NMR). Thus, NIRS and NMR in combination with chemometrics have become established non-destructive method for rapid assessment of quality parameters in the food and agricultural sectors. Therefore, the quality of macadamia kernel was assessed by NIRS and NMR using chemometric tools such as PCA-LDA and GA-LDA to evaluate external kernel defects. Macadamia kernels were classified as: 1 = good, marketable kernels without defects; 2 = kernels with discoloration; 3 = immature kernels; 4 = kernels affected by mold; and 5 = kernels with insect damage. Using NIRS, the GA-LDA resulted in an accuracy and specificity of 97.8% and 100%, respectively, to classify good kernels. On the other hand, PCA-LDA technique resulting in an accuracy higher than 68% and specificity of 97.2% to classify immature kernels. For NMR, PCA-LDA resulted in an accuracy higher than 83% and GA-LDA resulted in an accuracy of 100%, both to classify kernels with insect damage. NIRS and NMR spectroscopy can be successfully used to classify unshelled macadamia kernels based on the defects. However, NIRS out-performed NMR based on the higher accuracy results

Total anthocyanin content determination in intact açaí (Euterpe oleracea Mart.) and palmitero-juçara (Euterpe edulis Mart.) fruit using near infrared spectroscopy (NIR) and multivariate calibration

AbstractThe aim of this study was to evaluate near-infrared reflectance spectroscopy (NIR), and multivariate calibration potential as a rapid method to determinate anthocyanin content in intact fruit (açaí and palmitero-juçara). Several multivariate calibration techniques, including partial least squares (PLS), interval partial least squares, genetic algorithm, successive projections algorithm, and net analyte signal were compared and validated by establishing figures of merit. Suitable results were obtained with the PLS model (four latent variables and 5-point smoothing) with a detection limit of 6.2gkg−1, limit of quantification of 20.7gkg−1, accuracy estimated as root mean square error of prediction of 4.8gkg−1, mean selectivity of 0.79gkg−1, sensitivity of 5.04×10−3gkg−1, precision of 27.8gkg−1, and signal-to-noise ratio of 1.04×10−3gkg−1. These results suggest NIR spectroscopy and multivariate calibration can be effectively used to determine anthocyanin content in intact açaí and palmitero-juçara fruit

Conventional and alternative pre-harvest treatments affect the quality of ‘Golden Delicious’ and ‘York’ apple fruit

Apple trees cv. ‘Golden Delicious’ and ‘York’ were sprayed from bloom to fruit maturity with different products to evaluate the effect of pre-harvest treatments on fruit quality, including insect/disease damage and physicochemical fruit traits. Apple trees were assigned to five treatments: unsprayed (control), holistic solution (foliar nutrients and probiotics), insecticides, antimicrobials (fungicides and antibiotics), and a combination of antimicrobials + insecticides. The treatments started soon after bloom and were carried out every two weeks until fruit were ready to harvest. Diseases such as sooty blotch (complex of several fungi) and flyspeck (Zygophiala jamaicensis Mason) were the major source of damage on fruits. ‘Golden Delicious’ trees had a higher percentage of undamaged fruit than ‘York’, but all trees had some percentage of damaged fruit. Damage was most severe in the control (unsprayed) and insecticide treatments, intermediate in the holistic treatment, and much lower in the antimicrobial and antimicrobial + insecticide treatments (p < 0.003 for all comparisons). There was also a significant interactive effect (p < .0001) of cultivars and pre-harvest spray treatment on apple fruit mass. For both cultivars there was a strong effect of spray treatment on size, with larger apples produced in the antimicrobial and antimicrobial + insecticide treatments, but these effects were more pronounced in 'York' than in 'Golden Delicious' apples. ‘Golden Delicious’ trees produced 1.4-fold heavier and bigger fruits compared to ‘York’ and ‘Golden Delicious’ fruit were more mature than ‘York’ at harvest. Pre-harvest treatments also affected other quality parameters of apple fruit, such as soluble solids content (SSC) and starch-iodine index. Using partial least squares discriminant analysis (PLS-DA), ‘Golden Delicious’ fruit could be well classified according to the holistic, antimicrobial, and antimicrobial + insecticide treatments. Control and insecticide samples clustered together, indicating similarities between fruit quality. Overall, pre-harvest spray treatment affected the quality of ‘Golden Delicious’ and ‘York’ apples, mainly the fruit mass and disease infection

Estimation and classification of popping expansion capacity in popcorn breeding programs using NIR spectroscopy

One of the most important quality traits in popcorn breeding programs is the popping expansion (PE) capacity of the kernel, which is the ratio of the volume of the popcorn to the weight of the kernel. In this study, we evaluated whether near infrared spectroscopy (NIR spectroscopy) could be used as a tool in popcorn breeding programs to routinely predict and/or discriminate popcorn genotypes on the basis of their PE. Three generations (F1, F2, and F2:3) were developed in three planting seasons by manual cross-pollination and self-pollination. A total of 376 ears from the F2:3 generation were selected, shelled, and subjected to phenotypic analysis. Genetic variability was observed in the F2 and F2:3 generations, and their average PE value was 31.5 ± 6.7 mL.g-1. PE prediction models using partial least square (PLS) regression were developed, and the root mean square error of calibration (RMSEC) was 6.08 mL.g-1, while the coefficient of determination (RC 2) was 0.26. The model developed by principal component analysis with quadratic discriminant analysis (PCA-QDA) was the best for discriminating the kernels with low PE (≤ 30 mL.g-1) from those with high PE (> 30 mL.g-1) with an accuracy of 78%, sensitivity of 81.2%, and specificity of 72.2%. Although NIR spectroscopy appears to be a promising non-destructive method for assessing the PE of intact popcorn kernels for narrow breeding populations, greater variability and larger sample sizes would help improve the robustness of the predictive and classificatory models

Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program

In soybean (Glycine max L.) breeding programs, segregation is normally observed, and it is not possible to have replicates of individuals because each genotype is a unique copy. Therefore, near-infrared spectroscopy (NIRS) was used as a non-destructive tool to classify soybeans by genotypes and to predict oil content. A total of 260 soybean genotypes were divided into five classes, which were composed of 32, 52, 82, 46, and 49 samples of the BV, BVV, EB, JAB, and L class, respectively. NIR spectra were obtained using oven-dried samples (80 g) in a reflectance mode. A successive projection algorithm and genetic algorithm with linear discriminant analysis discriminated genotypes of the low (L class) from the high (EB class) for oil content (88.89% accuracy). The partial least square regression models for oil content were considered good (root mean square error of prediction of 0.96%). Therefore, NIRS can be used as a non-destructive tool in soybean breeding programs, but further investigation is necessary to improve the robustness of the models. It is important to note that to use the models, it is necessary to collect NIR spectra from dry soybean samples

O tempo de pega em gelatinas comerciais: uma experiência da disciplina de quimiometria para estudantes de graduação em Química Investigation of the setting-time of commercial gelatins: an experiment from a chemometric course for undergraduate chemistry students

The aim of this paper is to describe a chemometrics experiment for undergraduate chemistry students using basic concepts of experimental design to determine setting-time of commercial gelatins