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Rapid detection method for mango hardness

A detection method and mango technology, applied in measurement devices, color/spectral characteristic measurement, instruments, etc., can solve the problems of inability to perform non-destructive testing, time-consuming and labor-intensive, etc.

Inactive Publication Date: 2011-11-23
食品行业生产力促进中心
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

At present, the traditional method of testing the hardness of mango mainly uses sampling, which is time-consuming and laborious, and the fruit needs to be peeled during the test, so non-destructive testing cannot be achieved.

Method used

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  • Rapid detection method for mango hardness
  • Rapid detection method for mango hardness
  • Rapid detection method for mango hardness

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0025] Example: CCD-NIR non-destructive testing of mango firmness

[0026] Sample preparation: Mango samples were collected from 108 'Tainong' mangoes. Mangoes of different sizes and colors were selected during collection to make the range of hardness content as large as possible, and the representativeness of the samples was better. These mangoes were stored in a laboratory environment (~20° C.) for 5 hours after purchase, so that the temperature was consistent with the laboratory temperature.

[0027] Near-infrared spectrum collection: AvaSpec-2048CCD near-infrared spectrometer (Avantes Company) was used to measure the diffuse reflectance spectrum of mango samples. The light source is a halogen tungsten lamp (AvaLight-HAL), the detector is a 2048-pixel linear array CCD detector, equipped with a 75mm focusing optical platform, the spectral range is 580-1100nm, the CCD integration time is 2ms, and the number of scans is 100. BaSO with a diameter of 30mm and a thickness of 5mm...

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Abstract

In order to overcome the defects that the analysis method for detection of mango hardness is complex to operate and is laborious and time-consuming, and fruit bodies are damaged in the detection, the invention provides a rapid detection method for the mango hardness based on a near infrared spectroscopic analysis technology. The rapid detection method comprises the follow steps of: collecting mango samples for modeling; measuring the near infrared spectra of the samples; pre-processing the spectra; measuring the hardness of the mango samples by using a standard method; establishing a calibration model between the near infrared spectra and the mango hardness by using a chemometrics method; verifying and optimizing the model; collecting the near infrared spectra of the samples to be detected, and quantitatively detecting the hardness of the samples by using the model. The method provided by the invention is rapid in detection, has no damage to the fruit bodies, and provides a technical base for dynamic detection and tracking of mango quality in a logistic process.

Description

technical field [0001] The invention relates to a method for quickly detecting mango hardness, in particular to a method for rapidly measuring mango hardness by using a mathematical model between the mango near-infrared spectrum and the mango hardness index. Background technique [0002] Mango is a major tropical fruit in my country, and its hardness is an important indicator for judging the maturity of mangoes. It is of great significance for judging the picking period of mangoes and for the post-harvest preservation of mangoes. At present, the traditional method for testing the hardness of mangoes mainly uses sampling, which is time-consuming and laborious, and the fruit needs to be peeled during the test, so non-destructive testing cannot be achieved. Therefore, a simple, fast, and non-destructive detection method for mango hardness can not only effectively realize the on-tree detection and discrimination of mango maturity, guide the selection of the best picking period, ...

Claims

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Application Information

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IPC IPC(8): G01N21/25G01N21/35G06K9/00G01N21/3563G01N21/359
Inventor 屠振华孙丽娟温凯冯霖
Owner 食品行业生产力促进中心
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