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Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published: June 28th, 2016



1United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, 2Faculty of Agriculture, Tokyo University of Agriculture and Technology, 3Institute of Agriculture, Tokyo University of Agriculture and Technology

We present here a protocol to construct and validate models for nondestructive prediction of total sugar, total organic acid, and total anthocyanin content in individual blueberries by near-infrared spectroscopy.

Nondestructive prediction of ingredient contents of farm products is useful to ship and sell the products with guaranteed qualities. Here, near-infrared spectroscopy is used to predict nondestructively total sugar, total organic acid, and total anthocyanin content in each blueberry. The technique is expected to enable the selection of only delicious blueberries from all harvested ones. The near-infrared absorption spectra of blueberries are measured with the diffuse reflectance mode at the positions not on the calyx. The ingredient contents of a blueberry determined by high-performance liquid chromatography are used to construct models to predict the ingredient contents from observed spectra. Partial least squares regression is used for the construction of the models. It is necessary to properly select the pretreatments for the observed spectra and the wavelength regions of the spectra used for analyses. Validations are necessary for the constructed models to confirm that the ingredient contents are predicted with practical accuracies. Here we present a protocol to construct and validate the models for nondestructive prediction of ingredient contents in blueberries by near-infrared spectroscopy.

Near-infrared (NIR) spectroscopy is widely applied as a nondestructive technique to analyze contents of fruits and vegetables of various kinds.1,2 Nondestructive analyses by NIR spectroscopy enable the shipping of only delicious fruits and vegetables with guaranteed qualities. NIR spectroscopy has already been applied to orange, apple, melon, cherry, kiwi fruit, mango, papaya, peach and so on to know their Brix that corresponds to the total sugar content, acidity, TSC (total solids contents), and so on. Recently, we have reported the application of NIR spectroscopy to the quality evaluation of blueberries.3 We measured not only the total sugar co....

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1. Collection of Samples

  1. Decide which cultivars will be included in the target of the calibration model.
  2. Collect sufficient number and various types of sample blueberries of the target cultivars.
    1. Collect preferably 100 blueberries for the construction of the calibration model, and at least 10 for the validation of the constructed model. In order to construct robust models, collect samples of various types, i.e. with various colors, sizes, and at various ripening conditions.
    2. .......

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Figure 2 shows as an example a set of NIR absorption spectra of blueberries where spectra of 70 blueberries are shown simultaneously. Since the bands definitely assignable to sugars, organic acids, or anthocyanins are not observed in the NIR spectra, traditional Lambert-Beer's law is not applicable to quantify the ingredient contents. Therefore, the construction of models for the prediction of ingredient contents is necessary.

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Some additional comments on the protocol are described here. Firstly, in step 1.1, it is mentioned to decide the cultivars included in the target. Although it is possible to construct models covering blueberries from many cultivars or without specifying cultivars, the prediction accuracies with the models are sometimes much lower than those with the models for a single cultivar and for limited cultivars. It should also be noted that the calibration models should be constructed for blueberries from each production site to.......

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This work was partially supported by the project "A Scheme to Revitalize Agriculture and Fisheries in Disaster Area through Deploying Highly Advanced Technology" of Ministry of Agriculture, Forestry and Fisheries, Japan.


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Name Company Catalog Number Comments
FT-NIR spectrophotometer Bruker Optics GmbH MPA 
High-Performance Liquid Chromatography Shimadzu Corporation 228-45041-91, 228-45000-31, 228-45018-31, For sugar analysis
223-04500-31, 228-45010-31, 228-45095-31 Refractive Index Detector
High-Performance Liquid Chromatography Shimadzu Corporation 228-45041-91, 228-45003-31, 228-45000-31, For organic acid analysis
228-45018-31, 228-45010-31, 223-04500-31 Ultraviolet-Visible Detector
High-Performance Liquid Chromatography Shimadzu Corporation 228-45041-91, 228-45018-31, 228-45000-31, For anthocyanin analysis
228-45012-31, 228-45119-31, 228-45005-31, Photodiode Array Detector
pH meter Mettler-Toledo 30019028 S220, Automatic temperature compensation
Ultra-pure water treatment equipment ORGANO Corporation ORG-ULXXXM1; PRA-0015-0V0 PURELAB ultra; PURELITE
Biomedical Freezers  SANYO 2-6780-01 MDF-U338
Ultra-Low Temperature Freezer Panasonic healthcare Co.,Ltd. KM-DU73Y1 -80°C
Vacuum lyophilizer IWAKI GLASS Co.,Ltd 119770 DRC-3L;FRD-82M
Homoginizer Microtec Co., Ltd.  Physcotron
Ultracentrifuge Hitachi Koki Co.,Ltd S204567 CF15RXII
Mini-centrifuge LMS CO.,LTD. KN3136572 MCF-2360
Centrifuge Kokusan Co.,Ltd 2-5534-01 H-103N
Filter Paper  Advantec 1521070 5B, Eqivalent to Whatman 40
Sep-Pak C18 column Waters Corporation Milford WAT020515
Sep-Pak CM column Waters Corporation Milford WAT020550
Sep-Pak QMA column Waters Corporation Milford WAT020545
Centrifugal Filter Unit Merck Millipore Corporation R2SA18503 PVDF, 0.45 μm
Microtube As One Corporation 1-1600-02 PP, 2 mL
Syringe Filter GE Healthcare CO.,LTD. 6788-1304 PP, 0.45 μm
Sucrose Wako Pure Chemical Industries,Ltd 194-00011 Reagent-grade
Glucose Wako Pure Chemical Industries,Ltd 049-31165 Reagent-grade
Fructose Wako Pure Chemical Industries,Ltd 123-02762 Reagent-grade
Citric acid Wako Pure Chemical Industries,Ltd 036-05522 Reagent-grade
Malic acid Wako Pure Chemical Industries,Ltd 355-17971 Reagent-grade
Succinic acid  Wako Pure Chemical Industries,Ltd 190-04332 Reagent-grade
Quinic acid Alfa Aesar, A Johnson Matthey Company 10176328 Reagent-grade
Phosphoric acid Wako Pure Chemical Industries,Ltd 162-20492 HPLC-grade
Trifluoroacetic acid Wako Pure Chemical Industries,Ltd 208-02746 Reagent-grade
Methanol Wako Pure Chemical Industries,Ltd 131-01826 Reagent-grade
Acetonitrile Wako Pure Chemical Industries,Ltd 015-08633 HPLC-grade
Grade cyanidin-3-O-glucoside chloride Wako Pure Chemical Industries,Ltd 306-37661 HPLC-grade
Software for analyses Bruker Optics GmbH OPUS ver. 6.5
Softoware for preprocessing Microsoft Excel powered by Visual Basic for Applications
Software for construction of models Freemat 4.0

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