2018
DOI: 10.1177/0967033518811921
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Application of visible-near infrared spectroscopy to evaluate the quality of button mushrooms

Abstract: The Agaricus bisporus mushroom is one of the most cultivated and consumed mushrooms in the world, thanks to its delicacy, nutritional value and flavour. The quality evaluation of the A. bisporus during the harvest is generally established by a visual check by trained operators. This method complies with the request of the Distribution Channel (DC) to retailers and guarantees very low physical damage to the mushrooms; nevertheless, it is subjective and it does not guarantee the highest quality standard for the … Show more

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Cited by 13 publications
(9 citation statements)
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References 20 publications
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“…Mushrooms from the three flushes were harvested by hand and put directly in the baskets ready to be weighed. Each basket was then stored up inside a fridge to preserve mushroom's freshness (Giovenzana et al, 2019). The net yield deriving from the trial (excluded all the defective mushrooms) was 25,000 kg.…”
Section: Resultsmentioning
confidence: 99%
“…Mushrooms from the three flushes were harvested by hand and put directly in the baskets ready to be weighed. Each basket was then stored up inside a fridge to preserve mushroom's freshness (Giovenzana et al, 2019). The net yield deriving from the trial (excluded all the defective mushrooms) was 25,000 kg.…”
Section: Resultsmentioning
confidence: 99%
“…Ma et al 145 showed that quantitative modeling by NIR spectroscopy could accurately predict the polysaccharide content within Ganoderma lucidum mycelium. Giovenzana et al 146 showed that NIR can be used in combination with multivariate analysis to investigate mushroom quality and thus to predict key quality indicators to determine the appropriate time for harvesting objectively. Xu 147 used NIR spectroscopic analysis and chemometrics to establish a rapid identification method for conventional and aged mushrooms.…”
Section: Infrared Spectroscopymentioning
confidence: 99%
“…PCA can be done by eigenvalue decomposition or singular value decomposition of a data covariance matrix, usually after standardizing the attribute data. The outputs of a PCA are usually graphically discussed in terms of scores (the behaviour of the objects in the PCs space), loadings (the weight of the standardized original variables) and scores and loadings biplot (behaviour of the objects and weight of the original variables visualized in the standardized space of PCs) (Shaw, 2009;Olawoyin et al, 2014;Giovenzana et al, 2019).…”
Section: Life Cycle Impact Assessmentmentioning
confidence: 99%