Search

This study investigated the predictive validity and reliability of the load-velocity relationship with the one-repetition maximum test (1RM) in power clean from the knee exercise (PC). Initially, 12 healthy young males, with no PC experience, underwent eight sessions to learn the PC technique. After the learning period, the participants visited the laboratory on four more occasions with intervals between the visits from 72 to 96 h. The first two sessions were dedicated to the actual 1RM tests, while the last two sessions were performed to measure the barbell’s load-velocity relationship at 30%, 45%, 60%, 75%, and 90% of PC 1RM. The highest peak velocity recorded at each load was used to establish the linear regression equation and, consequently, to predict 1RM values. As a result, a low validity was observed between the highest actual 1RM value and the predicted 1RM in sessions 1 and 2 (typical errors = 3.6 to 5.0 kg, coefficients of variation = 6.03 to 8.21%, effect sizes = -1.23 to -1.00, and Bland-Altman bias = 8.5 to 11.7 kg). For reliability, higher measurement errors (intraclass correlation coefficient, typical error, coefficient of variation, and width of limits of agreement at 95%) were observed for the predicted 1RM compared to the actual 1RM test. In conclusion, the load-velocity relationship was not able to predict the 1RM value with high accuracy in the PC from the knee. Moreover, the predicted 1RM presented inferior reliability than the actual 1RM test.

Knowing the amount of water used during the growing of garlic (Allium sativum) is essential to optimize the efficiency of its use and to promote sustainable production, especially in drylands, where water resources are scarce. Therefore, the purpose of this study is to determine the water footprint (WF) in the cultivation of white, purple and red cultivars, in different areas of the province of Mendoza, Argentina. To account for the total water footprint (WFtotal) and each of its components (WFblue, WFgreenand WFgray), the methodology proposed by the Water Footprint Network was followed according to the Water Footprint Assessment Manual. The results show that the highest WFtotal in Mendoza corresponds to red garlic (973 m3 t–1) followed by white garlic (794 m3 t–1) and purple garlic (550 m3 t–1). Regarding the cultivation areas, it is observed that the highest values of WFtotal are found in the South region, followed by the North, Center and Valle de Uco regions respectively. On the other hand, the weighted WF value at the provincial level for the 3 varieties of garlic is 652 m3 t–1, where 20% corresponds to WFgreen, 58% to WFblue and finally 22% to WFgray. The variation of the WF results of each type of garlic and zones analyzed is associated mainly with climatic aspects and economics which determine the response to water consumption, productivity and yield., Conocer la cantidad de agua utilizada por el cultivo de ajo (Allium sativum) es primordial para optimizar la eficiencia en su uso y promover una producción sostenible, especialmente en tierras áridas, donde el recurso hídrico es escaso. Por ello, el propósito de este estudio es determinar la huella hídrica (HH) en la fase agrícola del ajo blanco, morado y colorado, en las diferentes zonas de cultivo en la provincia de Mendoza, Argentina. Para la determinación de la huella hídrica total y cada uno de sus componentes (HH azul, HH verde y HH gris) se siguió la metodología propuesta por la Water Footprint Network según el Manual de Evaluación de Huella Hídrica. Los resultados muestran que la mayor HH total promedio en Mendoza corresponde al ajo colorado (973 m3 t–1), seguido por el ajo blanco (794 m3 t–1) y el ajo morado (550 m3 t–1). En cuanto a las zonas de cultivo, se observa que los mayores valores de HH total se presentan en el sur, seguido por el norte, centro y Valle de Uco. Por otra parte, el valor de HH ponderada a nivel provincial para los 3 tipos de ajos es de 652 m3 t–1, donde el 20% corresponde a HH verde, 58% a HH azul y el 22% a la HH gris. La variación de los resultados obtenidos en cada tipo de ajo y zona considerada responde principalmente a aspectos climáticos y económicos que determinan la respuesta al consumo de agua, el rendimiento y la productividad.

Herbal teas like vine tea (Ampelopsis grossedentata) have been traditionally consumed worldwide because of their health-promotion and pleasant taste. Vine tea and its main bioactive component, dihydromyricetin, have gained attention because of their potential applications in food, material, and pharmaceutical sciences. Vine tea and dihydromyricetin have been suggested as potential natural antioxidants to extend shelf life of foods. Studies have also suggested potential application in packaging and food safety. Additionally, dietary supplementation with vine tea extract have shown great potential to prevent metabolic diseases, which can justify its application in novel functional foods. This review discusses the chemistry, functional properties, and potential applications of vine tea and dihydromyricetin in the food industry. Although vine tea extracts and dihydromyricetin have shown promising results, further studies on optimal application, thermal stability, synergetic effect with other natural antioxidants, consumer acceptability, and sensory profile of vine tea are needed to support food product innovation.

Herbal teas like vine tea (Ampelopsis grossedentata) have been traditionally consumed worldwide because of their health-promotion and pleasant taste. Vine tea and its main bioactive component, dihydromyricetin, have gained attention because of their potential applications in food, material, and pharmaceutical sciences. Vine tea and dihydromyricetin have been suggested as potential natural antioxidants to extend shelf life of foods. Studies have also suggested potential application in packaging and food safety. Additionally, dietary supplementation with vine tea extract have shown great potential to prevent metabolic diseases, which can justify its application in novel functional foods. This review discusses the chemistry, functional properties, and potential applications of vine tea and dihydromyricetin in the food industry. Although vine tea extracts and dihydromyricetin have shown promising results, further studies on optimal application, thermal stability, synergetic effect with other natural antioxidants, consumer acceptability, and sensory profile of vine tea are needed to support food product innovation.

Herbal teas like vine tea (Ampelopsis grossedentata) have been traditionally consumed worldwide because of their health-promotion and pleasant taste. Vine tea and its main bioactive component, dihydromyricetin, have gained attention because of their potential applications in food, material, and pharmaceutical sciences. Vine tea and dihydromyricetin have been suggested as potential natural antioxidants to extend shelf life of foods. Studies have also suggested potential application in packaging and food safety. Additionally, dietary supplementation with vine tea extract have shown great potential to prevent metabolic diseases, which can justify its application in novel functional foods. This review discusses the chemistry, functional properties, and potential applications of vine tea and dihydromyricetin in the food industry. Although vine tea extracts and dihydromyricetin have shown promising results, further studies on optimal application, thermal stability, synergetic effect with other natural antioxidants, consumer acceptability, and sensory profile of vine tea are needed to support food product innovation.

The study investigated the concurrent validity and reliability of the load-velocity relationship to predict the one-repetition maximum (1RM) of the power clean from the knee (PCK), high pull from the knee (HPK), and mid-thigh clean pull (MTCP). For each exercise, 12 participants performed two 1RM sessions tests and two sessions to measure the barbell’s load-velocity relationship at 30, 45, 60, 75, and 90% of 1RM. The velocity recorded at each load was used to establish the linear regression equation and, consequently, to predict 1RM value. A low validity between the 1RM direct test and predicted 1RM was observed for PCK (typical error [TE]=3.96 to 4.50 kg, coefficient of variation [CV]=4.68 to 5.27%, effect size [ES]=-0.76 to -0.58, Bland-Altman bias [BAB]=9.83 to 11.19 kg), HPK (TE=4.58 to 5.82 kg, CV=6.44 to 8.14%, ES=-0.40 to -0.39, BAB=3.52 to 4.17 kg), and MTCP (TE=6.33 to 8.08 kg, CV=4.78 to 6.16%, ES=-0.29 to -0.19, BAB=3.98 to 6.17 kg). Adequate reliability was observed for the 1RM direct test and for the predicted 1RM. However, based on Bland-Altman limits of agreement, lower measurement errors were obtained for the 1RM direct test in comparison to the predicted 1RM for all the exercises. In conclusion, the load-velocity relationship was not able to predict 1RM values with high accuracy in the PCK, HPK, and MTCP. Moreover, the 1RM direct test was the most reliable for PCK, HPK and MTCP.

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.

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.

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.

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 clu

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.

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.

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.

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.

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 clu

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 clu

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.

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.

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.

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.

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.

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.

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 clu

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.

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 clu

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.

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.

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 clu

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.

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.

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.

We have developed a rapid non-destructive method to assess the oxidative stability of intact macadamia nuts using near-infrared spectroscopy (NIRS). Intact macadamia nuts of the cultivars HAES 344 ‘Kau’, HAES 660 ‘Keaau’, IAC 4–12 B, and IAC Campinas B were harvested and immediately oven-dried for 4 days at 30 °C, 2 days at 40 °C, and 1 day at 60 °C to achieve 1.5% kernel moisture content. At each drying step nuts were withdrawn and their moisture content, peroxide value (PV), and acidity index (AI) determined. The best partial least square model for PV prediction was obtained using the Savitzky-Golay (SG) second derivative resulting in a standard error of prediction (SEP) of 0.55 meq·kg−1 and a coefficient of determination (R2C) of 0.57. The best AI prediction-model result was obtained using the SG second derivative (SEP = 0.14%, R2C = 0.29). Based on the maximum quality limits of 3 meq·kg−1 for PV and 0.5% for AI, the SEP values represented 18% and 28%, respectively. Therefore, the prediction method can be considered useful since the errors are lower than the quality limits. Thus, NIRS can be used to assess the oxidative stability of intact macadamia kernels.

We have developed a rapid non-destructive method to assess the oxidative stability of intact macadamia nuts using near-infrared spectroscopy (NIRS). Intact macadamia nuts of the cultivars HAES 344 ‘Kau’, HAES 660 ‘Keaau’, IAC 4–12 B, and IAC Campinas B were harvested and immediately oven-dried for 4 days at 30 °C, 2 days at 40 °C, and 1 day at 60 °C to achieve 1.5% kernel moisture content. At each drying step nuts were withdrawn and their moisture content, peroxide value (PV), and acidity index (AI) determined. The best partial least square model for PV prediction was obtained using the Savitzky-Golay (SG) second derivative resulting in a standard error of prediction (SEP) of 0.55 meq·kg−1 and a coefficient of determination (R2C) of 0.57. The best AI prediction-model result was obtained using the SG second derivative (SEP = 0.14%, R2C = 0.29). Based on the maximum quality limits of 3 meq·kg−1 for PV and 0.5% for AI, the SEP values represented 18% and 28%, respectively. Therefore, the prediction method can be considered useful since the errors are lower than the quality limits. Thus, NIRS can be used to assess the oxidative stability of intact macadamia kernels.

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.

We have developed a rapid non-destructive method to assess the oxidative stability of intact macadamia nuts using near-infrared spectroscopy (NIRS). Intact macadamia nuts of the cultivars HAES 344 ‘Kau’, HAES 660 ‘Keaau’, IAC 4–12 B, and IAC Campinas B were harvested and immediately oven-dried for 4 days at 30 °C, 2 days at 40 °C, and 1 day at 60 °C to achieve 1.5% kernel moisture content. At each drying step nuts were withdrawn and their moisture content, peroxide value (PV), and acidity index (AI) determined. The best partial least square model for PV prediction was obtained using the Savitzky-Golay (SG) second derivative resulting in a standard error of prediction (SEP) of 0.55 meq·kg−1 and a coefficient of determination (R2C) of 0.57. The best AI prediction-model result was obtained using the SG second derivative (SEP = 0.14%, R2C = 0.29). Based on the maximum quality limits of 3 meq·kg−1 for PV and 0.5% for AI, the SEP values represented 18% and 28%, respectively. Therefore, the prediction method can be considered useful since the errors are lower than the quality limits. Thus, NIRS can be used to assess the oxidative stability of intact macadamia kernels.

We have developed a rapid non-destructive method to assess the oxidative stability of intact macadamia nuts using near-infrared spectroscopy (NIRS). Intact macadamia nuts of the cultivars HAES 344 ‘Kau’, HAES 660 ‘Keaau’, IAC 4–12 B, and IAC Campinas B were harvested and immediately oven-dried for 4 days at 30 °C, 2 days at 40 °C, and 1 day at 60 °C to achieve 1.5% kernel moisture content. At each drying step nuts were withdrawn and their moisture content, peroxide value (PV), and acidity index (AI) determined. The best partial least square model for PV prediction was obtained using the Savitzky-Golay (SG) second derivative resulting in a standard error of prediction (SEP) of 0.55 meq·kg−1 and a coefficient of determination (R2C) of 0.57. The best AI prediction-model result was obtained using the SG second derivative (SEP = 0.14%, R2C = 0.29). Based on the maximum quality limits of 3 meq·kg−1 for PV and 0.5% for AI, the SEP values represented 18% and 28%, respectively. Therefore, the prediction method can be considered useful since the errors are lower than the quality limits. Thus, NIRS can be used to assess the oxidative stability of intact macadamia kernels.

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.

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.

We have developed a rapid non-destructive method to assess the oxidative stability of intact macadamia nuts using near-infrared spectroscopy (NIRS). Intact macadamia nuts of the cultivars HAES 344 ‘Kau’, HAES 660 ‘Keaau’, IAC 4–12 B, and IAC Campinas B were harvested and immediately oven-dried for 4 days at 30 °C, 2 days at 40 °C, and 1 day at 60 °C to achieve 1.5% kernel moisture content. At each drying step nuts were withdrawn and their moisture content, peroxide value (PV), and acidity index (AI) determined. The best partial least square model for PV prediction was obtained using the Savitzky-Golay (SG) second derivative resulting in a standard error of prediction (SEP) of 0.55 meq·kg−1 and a coefficient of determination (R2C) of 0.57. The best AI prediction-model result was obtained using the SG second derivative (SEP = 0.14%, R2C = 0.29). Based on the maximum quality limits of 3 meq·kg−1 for PV and 0.5% for AI, the SEP values represented 18% and 28%, respectively. Therefore, the prediction method can be considered useful since the errors are lower than the quality limits. Thus, NIRS can be used to assess the oxidative stability of intact macadamia kernels.

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.

We have developed a rapid non-destructive method to assess the oxidative stability of intact macadamia nuts using near-infrared spectroscopy (NIRS). Intact macadamia nuts of the cultivars HAES 344 ‘Kau’, HAES 660 ‘Keaau’, IAC 4–12 B, and IAC Campinas B were harvested and immediately oven-dried for 4 days at 30 °C, 2 days at 40 °C, and 1 day at 60 °C to achieve 1.5% kernel moisture content. At each drying step nuts were withdrawn and their moisture content, peroxide value (PV), and acidity index (AI) determined. The best partial least square model for PV prediction was obtained using the Savitzky-Golay (SG) second derivative resulting in a standard error of prediction (SEP) of 0.55 meq·kg−1 and a coefficient of determination (R2C) of 0.57. The best AI prediction-model result was obtained using the SG second derivative (SEP = 0.14%, R2C = 0.29). Based on the maximum quality limits of 3 meq·kg−1 for PV and 0.5% for AI, the SEP values represented 18% and 28%, respectively. Therefore, the prediction method can be considered useful since the errors are lower than the quality limits. Thus, NIRS can be used to assess the oxidative stability of intact macadamia kernels.

The objective of this study was to use dry matter (DM) calibration models to sort ‘Palmer’ mangoes prior cold storage and to evaluate the physiological and chemical changes during the storage period. PLS model developed with fruit from 2015/2016 season was not adequate to predict DM content in fruit from 2016/2017 (not adjusted R2). Therefore, VIS‐NIR spectra from 2016/2017 season were incorporated into data set and a new model was developed (RMSEcv of 10.5 g.kg−1, urn:x-wiley:01458892:media:jfpp13644:jfpp13644-math-0001 of 0.75). With the new model, ‘Palmer’ mangoes were sorted into two maturity stages (150 g.kg−1 and 110 g.kg−1) which resulted in quality differences mainly in relation to DM and SSC. Portable VIS‐NIR spectrometer can be used to sort fruit according to maturity stages based on DM content and this classification affect fruit quality during cold storage as fruit with higher DM (150 g.kg−1) presented better quality than fruit with lower DM (110 g.kg−1).

The objective of this study was to use dry matter (DM) calibration models to sort ‘Palmer’ mangoes prior cold storage and to evaluate the physiological and chemical changes during the storage period. PLS model developed with fruit from 2015/2016 season was not adequate to predict DM content in fruit from 2016/2017 (not adjusted R2). Therefore, VIS‐NIR spectra from 2016/2017 season were incorporated into data set and a new model was developed (RMSEcv of 10.5 g.kg−1, urn:x-wiley:01458892:media:jfpp13644:jfpp13644-math-0001 of 0.75). With the new model, ‘Palmer’ mangoes were sorted into two maturity stages (150 g.kg−1 and 110 g.kg−1) which resulted in quality differences mainly in relation to DM and SSC. Portable VIS‐NIR spectrometer can be used to sort fruit according to maturity stages based on DM content and this classification affect fruit quality during cold storage as fruit with higher DM (150 g.kg−1) presented better quality than fruit with lower DM (110 g.kg−1).

The objective of this study was to use dry matter (DM) calibration models to sort ‘Palmer’ mangoes prior cold storage and to evaluate the physiological and chemical changes during the storage period. PLS model developed with fruit from 2015/2016 season was not adequate to predict DM content in fruit from 2016/2017 (not adjusted R2). Therefore, VIS‐NIR spectra from 2016/2017 season were incorporated into data set and a new model was developed (RMSEcv of 10.5 g.kg−1, urn:x-wiley:01458892:media:jfpp13644:jfpp13644-math-0001 of 0.75). With the new model, ‘Palmer’ mangoes were sorted into two maturity stages (150 g.kg−1 and 110 g.kg−1) which resulted in quality differences mainly in relation to DM and SSC. Portable VIS‐NIR spectrometer can be used to sort fruit according to maturity stages based on DM content and this classification affect fruit quality during cold storage as fruit with higher DM (150 g.kg−1) presented better quality than fruit with lower DM (110 g.kg−1).

The objective of this study was to use dry matter (DM) calibration models to sort ‘Palmer’ mangoes prior cold storage and to evaluate the physiological and chemical changes during the storage period. PLS model developed with fruit from 2015/2016 season was not adequate to predict DM content in fruit from 2016/2017 (not adjusted R2). Therefore, VIS‐NIR spectra from 2016/2017 season were incorporated into data set and a new model was developed (RMSEcv of 10.5 g.kg−1, urn:x-wiley:01458892:media:jfpp13644:jfpp13644-math-0001 of 0.75). With the new model, ‘Palmer’ mangoes were sorted into two maturity stages (150 g.kg−1 and 110 g.kg−1) which resulted in quality differences mainly in relation to DM and SSC. Portable VIS‐NIR spectrometer can be used to sort fruit according to maturity stages based on DM content and this classification affect fruit quality during cold storage as fruit with higher DM (150 g.kg−1) presented better quality than fruit with lower DM (110 g.kg−1).

The objective of this study was to use dry matter (DM) calibration models to sort ‘Palmer’ mangoes prior cold storage and to evaluate the physiological and chemical changes during the storage period. PLS model developed with fruit from 2015/2016 season was not adequate to predict DM content in fruit from 2016/2017 (not adjusted R2). Therefore, VIS‐NIR spectra from 2016/2017 season were incorporated into data set and a new model was developed (RMSEcv of 10.5 g.kg−1, urn:x-wiley:01458892:media:jfpp13644:jfpp13644-math-0001 of 0.75). With the new model, ‘Palmer’ mangoes were sorted into two maturity stages (150 g.kg−1 and 110 g.kg−1) which resulted in quality differences mainly in relation to DM and SSC. Portable VIS‐NIR spectrometer can be used to sort fruit according to maturity stages based on DM content and this classification affect fruit quality during cold storage as fruit with higher DM (150 g.kg−1) presented better quality than fruit with lower DM (110 g.kg−1).

The objective of this study was to use dry matter (DM) calibration models to sort ‘Palmer’ mangoes prior cold storage and to evaluate the physiological and chemical changes during the storage period. PLS model developed with fruit from 2015/2016 season was not adequate to predict DM content in fruit from 2016/2017 (not adjusted R2). Therefore, VIS‐NIR spectra from 2016/2017 season were incorporated into data set and a new model was developed (RMSEcv of 10.5 g.kg−1, urn:x-wiley:01458892:media:jfpp13644:jfpp13644-math-0001 of 0.75). With the new model, ‘Palmer’ mangoes were sorted into two maturity stages (150 g.kg−1 and 110 g.kg−1) which resulted in quality differences mainly in relation to DM and SSC. Portable VIS‐NIR spectrometer can be used to sort fruit according to maturity stages based on DM content and this classification affect fruit quality during cold storage as fruit with higher DM (150 g.kg−1) presented better quality than fruit with lower DM (110 g.kg−1).