scholarly journals Interactions of Linearly Polarized and Unpolarized Light on Kiwifruit Using Aquaphotomics

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 494
Author(s):  
Damenraj Rajkumar ◽  
Rainer Künnemeyer ◽  
Harpreet Kaur ◽  
Jevon Longdell ◽  
Andrew McGlone
Keyword(s):  
Near Infrared ◽  
Polarized Light ◽  
Bound Water ◽  
Free Water ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Unpolarized Light ◽  
Standard Normal Variate ◽  
Linearly Polarized ◽  
Solids Content

Near infrared (NIR) spectroscopy is an important tool for predicting the internal qualities of fruits. Using aquaphotomics, spectral changes between linearly polarized and unpolarized light were assessed on 200 commercially grown yellow-fleshed kiwifruit (Actinidia chinensis var. chinensis ‘Zesy002’). Measurements were performed on different configurations of unpeeled (intact) and peeled (cut) kiwifruit using a commercial handheld NIR instrument. Absorbance after applying standard normal variate (SNV) and second derivative Savitzky–Golay filters produced different spectral features for all configurations. An aquagram depicting all configurations suggests that linearly polarized light activated more free water states and unpolarized light activated more bound water states. At depth (≥1 mm), after several scattering events, all radiation is expected to be fully depolarized and interactions for incident polarized or unpolarized light will be similar, so any observed differences are attributable to the surface layers of the fruit. Aquagrams generated in terms of the fruit soluble solids content (SSC) were similar for all configurations, suggesting the SSC in fruit is not a contributing factor here.

Near infrared spectroscopy for rapid determination of solids content of amino resins

2020 ◽  
Vol 28 (5-6) ◽  
pp. 344-350
Author(s):  
M Gonçalves ◽  
NT Paiva ◽  
JM Ferra ◽  
J Martins ◽  
F Magalhães ◽  
...  
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Water Addition ◽  
Reliable Technique ◽  
Standard Normal Variate ◽  
And Performance ◽  
Solids Content

Near infrared (NIR) spectroscopy is a fast and reliable technique for assessing properties of amino resins. One important property that defines the cost and performance of these resins is the solids content (SC). This work studied the prediction of SC of amino resins by combining NIR spectroscopy with partial least squares (PLS) regression. A total of 990 industrial NIR spectra of amino resins were obtained and split randomly by a ratio of 2/3 for calibration and 1/3 for validation. The best model achieved a root mean-square error of prediction (RMSEP) of 0.32% (m/m) and a coefficient of determination of prediction ([Formula: see text]) of 81%. standard normal variate (SNV) was found to be the NIR pre-processing that provided the best results for model construction. Addition of water to two amino resins showed that the NIR model does not respond to the water addition, despite water making great contribution to the SC value. An inference that can be obtained from this is that the NIR model of amino resins uses NIR properties of amino resins that relate to the SC and from there predict the most probable SC, instead of looking at all the components that affect the SC of amino resins.

A simple multiple linear regression model in near infrared spectroscopy for soluble solids content of pomegranate arils based on stability competitive adaptive re-weighted sampling

2021 ◽  
pp. 096703352098236
Author(s):  
Zhaoqiong Jiang ◽  
Yiping Du ◽  
Fangping Cheng ◽  
Feiyu Zhang ◽  
Wuye Yang ◽  
...  
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Pls Regression ◽  
Variable Selection Method ◽  
Solids Content ◽  
Pomegranate Arils ◽  
Mlr Model

The objective of this study was to develop a multiple linear regression (MLR) model using near infrared (NIR) spectroscopy combined with chemometric techniques for soluble solids content (SSC) in pomegranate samples at different storage periods. A total of 135 NIR diffuse reflectance spectra with the wavelength range of 950-1650 nm were acquired from pomegranate arils. Based upon sampling error profile analysis (SEPA), outlier diagnosis was conducted to improve the stability of the model, and four outliers were removed. Several pretreatment and variable selection methods were compared using partial least squares (PLS) regression models. The overall results demonstrated that the pretreatment method of the first derivative (1D) was very effective and the variable selection method of stability competitive adaptive re-weighted sampling (SCARS) was powerful for extracting feature variables. The equilibrium performance of 1D-SCARS-PLS regression model for ten times was similar to 1D-PLS regression model, so that the advantage of wavelength selection was inconspicuous in PLS regression model. However, the number of variables selected by 1D-SCARS was less to 9, which was enough to establish a simple MLR model. The performance of MLR model for SSC of pomegranate arils based on 1D-SCARS was receivable with the root-mean-square error of calibration set (RMSEC) of 0.29% and prediction set (RMSEP) of 0.31%. This strategy combining variable selection method with MLR may have a broad prospect in the application of NIR spectroscopy due to its simplicity and robustness.

scholarly journals NIR Analysis of Intact Grape Berries: Chemical and Physical Properties Prediction Using Multivariate Analysis

Foods ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 113
Author(s):  
Teodora Basile ◽  
Antonio Domenico Marsico ◽  
Rocco Perniola
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Software Environment ◽  
Specific Level ◽  
Grape Berries ◽  
Overall Acceptability ◽  
Texture Characteristics ◽  
Solids Content ◽  
Level Of Experience

Texture characteristics are valuable parameters in the perceived quality and overall acceptability of fresh fruit. The characterization of grape texture attributes, such as firmness and crunchiness, is usually performed by sensory analysis or instrumental texture analysis. Both methodologies are destructive. Hence, it is not possible to test multiple times or perform any other analysis on the same sample. In this article, near-infrared (NIR) spectroscopy was applied to intact berries of table grape cv. Regal Seedless. NIR spectra were employed to predict both the physical parameter “hardness”, which is correlated with the crunchiness of berry flesh and the sweetness, which is correlated with the total soluble solids content (TSS, as °Brix). The chemometric analysis was carried out exclusively based on an open-source software environment, producing results readily usable for any operator, besides the specific level of experience with NIR spectroscopy.

scholarly journals Near infrared spectroscopy determination of chemical and sensory properties in tomato

2021 ◽  
pp. 096703352110187
Author(s):  
Dong Sun ◽  
Jordi Cruz ◽  
Manel Alcalà ◽  
Roser Romero del Castillo ◽  
Silvia Sans ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Sensory Properties ◽  
Soluble Solids ◽  
Taste Intensity ◽  
Relative Standard Error ◽  
Tomato Puree ◽  
Relative Standard ◽  
Solids Content ◽  
Chemical Traits

Fast and massive characterization of quality attributes in tomatoes is a necessary step toward its improvement; for sensory attributes this process is time-consuming and very expensive, which causes its absence in routine phenotpying. We aimed to assess the feasibility of near infrared (NIR) spectroscopy as a fast and economical tool to predict both the chemical and sensory properties of tomatoes. We built partial least squares models from spectra recorded from tomato puree and juice in 53 genetically diverse varieties grown in two environments. Samples were divided in calibration (210 samples for chemical traits, 45 samples for sensory traits) and validation sets (60 and 10, respectively) using the Kennard Stone algorithm. Models from puree spectra gave validation r2 values higher than 0.97 for fructose, glucose, soluble solids content, and dry matter (relative standard error of prediction, RSEP% ranged 3.5–5.8), while r2 values for sensory properties were lower (ranging 0.702–0.917 for taste-related traits (RSEP%: 9.1–20.0), and 0.009–0.849 for texture related traits (RSEP%: 3.6–72.1)). For sensory traits such as explosiveness, juiciness, sweetness, acidity, taste intensity, aroma intensity, and mealiness, NIR spectroscopy is potentially useful for scanning large collections of samples to identify likely candidates to select for tomato quality.

scholarly journals The overall precision test for near infrared scanning and reference method for the determination of soluble solids content and pH of mango for processing factory

2020 ◽  
Vol 187 ◽  
pp. 04003
Author(s):  
Nphatsanan Saksangium ◽  
Panmanas Sirisomboon
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Reference Test ◽  
Infrared Scanning ◽  
Solids Content ◽  
Precision Test

Near infrared (NIR) spectroscopy is a rapid technique for nondestructive testing. Mango is popular fruit in Thailand. Therefore, The main aim of this paper is to report an overall precision of the NIR spectroscopy instruments and reference methods for determination at the beginning of the experiment for prediction models development to be in the mango applied processing factory.. Results showed that the repeatability of FT-NIR spectrometer and UV-VIS-NIR spectrometer were 0.00191 and 0.00529, respectively. The reproducibility of FT-NIR spectrometer and UV-VIS-NIR spectrometer were 0.00323 and 0.03561, respectively. Repeatability of reference test of TSS and pH were 0.1657 and 0.0827. Therefore, the R2max of TSS and pH were 0.9825 and 0.9504 which indicates that it is possible to develop NIR model for prediction of total soluble solids and pH.

Nondestructive Detection of Soluble Solids Content of Nanfeng Mandarin Orange Using VIS-NIR Spectroscopy

2011 ◽  
Vol 361-363 ◽  
pp. 1634-1637 ◽  
Author(s):  
Lu Zhang ◽  
Long Xue ◽  
Mu Hua Liu ◽  
Jing Li
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Rapid Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Mandarin Orange

This study demonstrated how VIS-NIR spectroscopy can be used in the quantitative, noninvasive probing of soluble solids content (SSC) of mandarin orange. Total 197 mandarin oranges were divided into calibration set (133 samples) and prediction set (64 samples). Multiple scatter correction (MSC) was used to preprocess the collected visible and near infrared (Vis-NIR) spectra (350-1800nm) of mandarin orange. Partial least square (PLS), interval partial least square (IPLS) and synergy interval partial least square (SIPLS) methods were applied for constructing predictive models of SSC. Experimental results showed that the optimal SIPLS model obtained with 10 PLS components and the optimal combinations of intervals were number 5,7,8,9. The correlation coefficient (r) between the predicted and actual SSC was 0.9265 and 0.8577 for calibration and prediction set, respectively. The root mean square error of calibration (RMSEC) and prediction (RMSEP) set was 0.4890 and 0.7113, respectively. In conclusion, the combination of Vis-NIR spectroscopy and SIPLS methods can be used to provide a technique of noninvasive, convenient and rapid analysis for SSC in fruit.

scholarly journals Application of the Non-Destructive NIR Technique for the Evaluation of Strawberry Fruits Quality Parameters

Foods ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 441 ◽  
Author(s):  
Manuela Mancini ◽  
Luca Mazzoni ◽  
Francesco Gagliardi ◽  
Francesca Balducci ◽  
Daniele Duca ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Infrared Region ◽  
Quality Parameters ◽  
Soluble Solids ◽  
Electromagnetic Spectrum ◽  
Least Squares Regression ◽  
Solids Content ◽  
Non Destructive

The determination of strawberry fruit quality through the traditional destructive lab techniques has some limitations related to the amplitude of the samples, the timing and the applicability along all phases of the supply chain. The aim of this study was to determine the main qualitative characteristics through traditional lab destructive techniques and Near Infrared Spectroscopy (NIR) in fruits of five strawberry genotypes. Principal Component Analysis (PCA) was applied to search for spectral differences among all the collected samples. A Partial Least Squares regression (PLS) technique was computed in order to predict the quality parameters of interest. The PLS model for the soluble solids content prediction was the best performing—in fact, it is a robust and reliable model and the validation values suggested possibilities for its use in quality applications. A suitable PLS model is also obtained for the firmness prediction—the validation values tend to worsen slightly but can still be accepted in screening applications. NIR spectroscopy represents an important alternative to destructive techniques, using the infrared region of the electromagnetic spectrum to investigate in a non-destructive way the chemical–physical properties of the samples, finding remarkable applications in the agro-food market.

scholarly journals Online quantitative analysis of soluble solids content in navel oranges using visible-near infrared spectroscopy and variable selection methods

2014 ◽  
Vol 07 (06) ◽  
pp. 1350065 ◽  
Author(s):  
Yande Liu ◽  
Yanrui Zhou ◽  
Yuanyuan Pan
Keyword(s):  
Quantitative Analysis ◽  
Variable Selection ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Internal Quality ◽  
Soluble Solids Content ◽  
Selection Methods ◽  
Calibration Models ◽  
Solids Content

Variable selection is applied widely for visible-near infrared (Vis-NIR) spectroscopy analysis of internal quality in fruits. Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content (SSC) in navel oranges. Moving window partial least squares (MW-PLS), Monte Carlo uninformative variables elimination (MC-UVE) and wavelet transform (WT) combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges. The performances of these methods were compared for modeling the Vis-NIR data sets of navel orange samples. Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation coefficient (r) of 0.89 and lower root mean square error of prediction (RMSEP) of 0.54 at 5 fruits per second. It concluded that Vis-NIR spectroscopy coupled with WT-MC-UVE may be a fast and effective tool for online quantitative analysis of SSC in navel oranges.

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