Yiyi Zhang, Jacqueline F. Nock, Yosef Al Shoffe, Christopher B. Watkins
{"title":"近红外光谱无损预测8个苹果品种可溶性固形物和干物质含量","authors":"Yiyi Zhang, Jacqueline F. Nock, Yosef Al Shoffe, Christopher B. Watkins","doi":"10.1016/j.postharvbio.2019.01.009","DOIUrl":null,"url":null,"abstract":"<div><p>Soluble solids content (SSC) is an important factor for assessing quality of apples as it is linked to consumer taste preferences. Fruit dry matter content (DMC) is dominated by soluble sugar and starch concentrations at harvest, and therefore the DMC at the time of harvest can be strongly correlated with the post-storage SSC. The objective of this study was to develop models based on near-infrared (NIR) spectroscopy using a commercially available handheld instrument to predict SSC and DMC of fruit at harvest and after storage. ‘Gala’, ‘Honeycrisp’, ‘McIntosh’, ‘Jonagold’, ‘NY1′, ‘NY2′, ‘Red Delicious’ and ‘Fuji’ apples were tested. Partial least square regression was used to build calibration models for prediction of SSC and DMC. Models were also built for individual and multiple cultivars. Internal and external validations were applied to test the accuracy and precision of both models. In general, the individual- and multi-cultivar models have similar calibration performance. In internal validations, R<sup>2</sup> and RMSE from multi-cultivar and individual-cultivar models were similar, but the slope values were higher in individual-cultivar than multi-cultivar models, indicating that the prediction using individual-cultivar model was more accurate. However, for individual-cultivar models, data-overfitting and the reference values distribution may lead to poor prediction in external validation. Overall the results support use of a portable NIR-based instrument to predict SSC and DMC, but to obtain precision and accurate predictions, calibration models should be built based on individual cultivars and the variability from seasonal and regional effects have to be taken into consideration.</p></div>","PeriodicalId":20328,"journal":{"name":"Postharvest Biology and Technology","volume":null,"pages":null},"PeriodicalIF":6.4000,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.postharvbio.2019.01.009","citationCount":"47","resultStr":"{\"title\":\"Non-destructive prediction of soluble solids and dry matter contents in eight apple cultivars using near-infrared spectroscopy\",\"authors\":\"Yiyi Zhang, Jacqueline F. Nock, Yosef Al Shoffe, Christopher B. Watkins\",\"doi\":\"10.1016/j.postharvbio.2019.01.009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Soluble solids content (SSC) is an important factor for assessing quality of apples as it is linked to consumer taste preferences. Fruit dry matter content (DMC) is dominated by soluble sugar and starch concentrations at harvest, and therefore the DMC at the time of harvest can be strongly correlated with the post-storage SSC. The objective of this study was to develop models based on near-infrared (NIR) spectroscopy using a commercially available handheld instrument to predict SSC and DMC of fruit at harvest and after storage. ‘Gala’, ‘Honeycrisp’, ‘McIntosh’, ‘Jonagold’, ‘NY1′, ‘NY2′, ‘Red Delicious’ and ‘Fuji’ apples were tested. Partial least square regression was used to build calibration models for prediction of SSC and DMC. Models were also built for individual and multiple cultivars. Internal and external validations were applied to test the accuracy and precision of both models. In general, the individual- and multi-cultivar models have similar calibration performance. In internal validations, R<sup>2</sup> and RMSE from multi-cultivar and individual-cultivar models were similar, but the slope values were higher in individual-cultivar than multi-cultivar models, indicating that the prediction using individual-cultivar model was more accurate. However, for individual-cultivar models, data-overfitting and the reference values distribution may lead to poor prediction in external validation. Overall the results support use of a portable NIR-based instrument to predict SSC and DMC, but to obtain precision and accurate predictions, calibration models should be built based on individual cultivars and the variability from seasonal and regional effects have to be taken into consideration.</p></div>\",\"PeriodicalId\":20328,\"journal\":{\"name\":\"Postharvest Biology and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.4000,\"publicationDate\":\"2019-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.postharvbio.2019.01.009\",\"citationCount\":\"47\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Postharvest Biology and Technology\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0925521418305672\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Postharvest Biology and Technology","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925521418305672","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
Non-destructive prediction of soluble solids and dry matter contents in eight apple cultivars using near-infrared spectroscopy
Soluble solids content (SSC) is an important factor for assessing quality of apples as it is linked to consumer taste preferences. Fruit dry matter content (DMC) is dominated by soluble sugar and starch concentrations at harvest, and therefore the DMC at the time of harvest can be strongly correlated with the post-storage SSC. The objective of this study was to develop models based on near-infrared (NIR) spectroscopy using a commercially available handheld instrument to predict SSC and DMC of fruit at harvest and after storage. ‘Gala’, ‘Honeycrisp’, ‘McIntosh’, ‘Jonagold’, ‘NY1′, ‘NY2′, ‘Red Delicious’ and ‘Fuji’ apples were tested. Partial least square regression was used to build calibration models for prediction of SSC and DMC. Models were also built for individual and multiple cultivars. Internal and external validations were applied to test the accuracy and precision of both models. In general, the individual- and multi-cultivar models have similar calibration performance. In internal validations, R2 and RMSE from multi-cultivar and individual-cultivar models were similar, but the slope values were higher in individual-cultivar than multi-cultivar models, indicating that the prediction using individual-cultivar model was more accurate. However, for individual-cultivar models, data-overfitting and the reference values distribution may lead to poor prediction in external validation. Overall the results support use of a portable NIR-based instrument to predict SSC and DMC, but to obtain precision and accurate predictions, calibration models should be built based on individual cultivars and the variability from seasonal and regional effects have to be taken into consideration.
期刊介绍:
The journal is devoted exclusively to the publication of original papers, review articles and frontiers articles on biological and technological postharvest research. This includes the areas of postharvest storage, treatments and underpinning mechanisms, quality evaluation, packaging, handling and distribution of fresh horticultural crops including fruit, vegetables, flowers and nuts, but excluding grains, seeds and forages.
Papers reporting novel insights from fundamental and interdisciplinary research will be particularly encouraged. These disciplines include systems biology, bioinformatics, entomology, plant physiology, plant pathology, (bio)chemistry, engineering, modelling, and technologies for nondestructive testing.
Manuscripts on fresh food crops that will be further processed after postharvest storage, or on food processes beyond refrigeration, packaging and minimal processing will not be considered.