scholarly journals NIR model development and robustness in prediction of melon fruit total soluble solids

2006 ◽  
Vol 57 (4) ◽  
pp. 411 ◽  
Author(s):  
J. A. Guthrie ◽  
C. J. Liebenberg ◽  
K. B. Walsh
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Mean Squared Error ◽  
Scatter Correction ◽  
Model Development ◽  
Equatorial Region ◽  
Soluble Solids ◽  
Eating Quality ◽  
Spectral Window ◽  
Total Soluble Solids

Near infrared spectroscopy (NIRS) can be used for the on-line, non-invasive assessment of fruit for eating quality attributes such as total soluble solids (TSS). The robustness of multivariate calibration models, based on NIRS in a partial transmittance optical geometry, for the assessment of TSS of intact rockmelons (Cucumis melo) was assessed. The mesocarp TSS was highest around the fruit equator and increased towards the seed cavity. Inner mesocarp TSS levels decreased towards both the proximal and distal ends of the fruit, but more so towards the proximal end. The equatorial region of the fruit was chosen as representative of the fruit for near infrared assessment of TSS. The spectral window for model development was optimised at 695–1045 nm, and the data pre-treatment procedure was optimised to second-derivative absorbance without scatter correction. The ‘global’ modified partial least squares (MPLS) regression modelling procedure of WINISI (ver. 1.04) was found to be superior with respect to root mean squared error of prediction (RMSEP) and bias for model predictions of TSS across seasons, compared with the ‘local’ MPLS regression procedure. Updating of the model with samples selected randomly from the independent validation population demonstrated improvement in both RMSEP and bias with addition of approximately 15 samples.

scholarly journals Assessment of internal quality attributes of mandarin fruit. 1. NIR calibration model development

2005 ◽  
Vol 56 (4) ◽  
pp. 405 ◽  
Author(s):  
J. A. Guthrie ◽  
K. B. Walsh ◽  
D. J. Reid ◽  
C. J. Liebenberg
Keyword(s):  
Scatter Correction ◽  
Model Development ◽  
Soluble Solids ◽  
Internal Quality ◽  
Invasive Technique ◽  
Eating Quality ◽  
Calibration Model ◽  
Equatorial Position ◽  
Spectral Window ◽  
Partial Least Squares Model

The utility of near infrared spectroscopy as a non-invasive technique for the assessment of internal eating quality parameters of mandarin fruit (Citrus reticulata cv. Imperial) was assessed. The calibration procedure for the attributes of TSS (total soluble solids) and DM (dry matter) was optimised with respect to a reference sampling technique, scan averaging, spectral window, data pre-treatment (in terms of derivative treatment and scatter correction routine) and regression procedure. The recommended procedure involved sampling of an equatorial position on the fruit with 1 scan per spectrum, and modified partial least squares model development on a 720–950-nm window, pre-treated as first derivative absorbance data (gap size of 4 data points) with standard normal variance and detrend scatter correction. Calibration model performance for the attributes of TSS and DM content was encouraging (typical Rc2 of >0.75 and 0.90, respectively; typical root mean squared standard error of calibration of <0.4 and 0.6%, respectively), whereas that for juiciness and total acidity was unacceptable. The robustness of the TSS and DM calibrations across new populations of fruit is documented in a companion study.

scholarly journals Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness

2005 ◽  
Vol 56 (4) ◽  
pp. 417 ◽  
Author(s):  
J. A. Guthrie ◽  
D. J. Reid ◽  
K. B. Walsh
Keyword(s):  
Near Infrared ◽  
Model Updating ◽  
Multivariate Calibration ◽  
Mean Squared Error ◽  
Sample Selection ◽  
Model Performance ◽  
Soluble Solids ◽  
Internal Quality ◽  
Calibration Model ◽  
Validation Population

The robustness of multivariate calibration models, based on near infrared spectroscopy, for the assessment of total soluble solids (TSS) and dry matter (DM) of intact mandarin fruit (Citrus reticulata cv. Imperial) was assessed. TSS calibration model performance was validated in terms of prediction of populations of fruit not in the original population (different harvest days from a single tree, different harvest localities, different harvest seasons). Of these, calibration performance was most affected by validation across seasons (signal to noise statistic on root mean squared error of prediction of 3.8, compared with 20 and 13 for locality and harvest day, respectively). Procedures for sample selection from the validation population for addition to the calibration population (‘model updating’) were considered for both TSS and DM models. Random selection from the validation group worked as well as more sophisticated selection procedures, with approximately 20 samples required. Models that were developed using samples at a range of temperatures were robust in validation for TSS and DM.

scholarly journals Feasibility Study of the Use of Handheld NIR Spectrometer for Simultaneous Authentication and Quantification of Quality Parameters in Intact Pineapple Fruits

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Charles L. Y. Amuah ◽  
Ernest Teye ◽  
Francis Padi Lamptey ◽  
Kwasi Nyandey ◽  
Jerry Opoku-Ansah ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Multivariate Calibration ◽  
Principal Component ◽  
Quality Parameters ◽  
Soluble Solids ◽  
Least Squares Regression ◽  
Total Soluble Solids ◽  
Linear Discriminant ◽  
Identification Rate

The potential of predicting maturity using total soluble solids (TSS) and identifying organic from inorganic pineapple fruits based on near-infrared (NIR) spectra fingerprints would be beneficial to farmers and consumers alike. In this study, a portable NIR spectrometer and chemometric techniques were combined to simultaneously identify organically produced pineapple fruits from conventionally produced ones (thus organic and inorganic) and also predict total soluble solids. A total of 90 intact pineapple fruits were scanned with the NIR spectrometer while a digital refractometer was used to measure TSS from extracted pineapple juice. After attempting several preprocessing techniques, multivariate calibration models were built using principal component analysis (PCA), K-nearest neighbor (KNN), and linear discriminant analysis (LDA) to identify the classes (organic and conventional pineapple fruits) while partial least squares regression (PLSR) method was used to determine TSS of the fruits. Among the identification techniques, the MSC-PCA-LDA model accurately identified organic from conventionally produced fruits at 100% identification rate. For quantification of TSS, the MSC-PLSR model gave Rp = 0.851 and RMSEC = 0.950 °Brix, and Rc = 0.854 and RMSEP = 0.842 °Brix at 5 principal components in the calibration set and prediction set, respectively. The results generally indicated that portable NIR spectrometer coupled with the appropriate chemometric tools could be employed for rapid nondestructive examination of pineapple quality and also to detect pineapple fraud due to mislabeling of conventionally produced fruits as organic ones. This would be helpful to farmers, consumers, and quality control officers.

scholarly journals Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Lin Zhang ◽  
Baohua Zhang ◽  
Jun Zhou ◽  
Baoxing Gu ◽  
Guangzhao Tian
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Near Infrared ◽  
Scatter Correction ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Biological Variability ◽  
Wavelength Selection ◽  
Calibration Models ◽  
Solids Content

Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transform near-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability.

Limitations to the measurement of intact melon total soluble solids using near infrared spectroscopy

2006 ◽  
Vol 57 (4) ◽  
pp. 403 ◽  
Author(s):  
Robert L. Long ◽  
Kerry B. Walsh
Keyword(s):  
Near Infrared ◽  
Model Performance ◽  
Nir Spectroscopy ◽  
Quality Standard ◽  
Sampling Procedure ◽  
Performance Model ◽  
Soluble Solids ◽  
Fruit Storage ◽  
Total Soluble Solids ◽  
Fruit Maturity

The imposition of a minimum total soluble solids (TSS) value as a quality standard for orange-flesh netted melon fruit (Cucumis melo L. reticulatus group) requires either a batch sampling procedure (i.e. the estimation of the mean and standard deviation of a population), or the individual assessment of fruit [e.g. using a non-destructive procedure such as near infrared (NIR) spectroscopy]. Several potential limitations to the NIR assessment of fruit, including the variation in TSS within fruit and the effect of fruit storage conditions on the robustness of calibration models, were considered in this study. Outer mesocarp TSS was 3 TSS units higher at the stylar end of the fruit compared with the stem end, and the TSS of inner mesocarp was higher than outer tissue and more uniform across spatial positions. The linear relationship between the outer 10 mm and the subsequent middle 10 mm of tissue varied with fruit maturity [e.g. 42 days before harvest (DBH), r 2 = 0.8; 13 DBH, r 2 = 0.4; 0 DBH, r 2 = 0.7], and with cultivars (at fruit maturity, Eastern Star 2001 r 2 = 0.88; Malibu 2001 r 2 = 0.59). This relationship notably affected NIR calibration performance (e.g. based on inner mesocarp TSS; R c 2 = 0.80, root mean standard error of cross-validation (RMSECV) = 0.65, and R c 2 = 0.41, RMSECV = 0.88 for mature Eastern Star and Malibu fruit, respectively). Cold storage of fruit (0–14 days at 5°C) did not affect NIR model performance. Model performance was equivalent when based on either that part of the fruit in contact with the ground or equatorial positions; however, it was improved when based on the stylar end of the fruit.

Multivariate Calibration and Chemometrics for near Infrared Spectroscopy: Which Method?

2000 ◽  
Vol 8 (4) ◽  
pp. 229-237 ◽  
Author(s):  
Pierre Dardenne ◽  
George Sinnaeve ◽  
Vincent Baeten
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Scatter Correction ◽  
Large Data ◽  
Data Sets ◽  
Agricultural Commodity ◽  
Regression Methods ◽  
Standard Normal Variate ◽  
Pre Treatment ◽  
Artificial Neural Network Ann

The four most important regression methods are evaluated on very large data sets: Multiple Linear Regression (MLR), Partial Least Squares (PLS), Artificial Neural Network (ANN) and a new concept called “LOCAL” (PLS with selection of a calibration sample subset of the closest neighbours for each sample to predict). The Standard Errors of Prediction ( SEPs) are statistically tested and the results show that the regression methods are almost equal and that the data matrices are more important than the fitting methods themselves. The types of pre-treatments (Multiplicative Scatter Correction, Detrend, Standard Normal Variate, derivative etc.) of the spectra are too numerous to be able to test all the combinations. For each test, the pre-treatment found as the best with the PLS method is fixed for the other ones. The second part of the paper emphasises the importance of the number of samples. If any agricultural commodity, and probably any kind of product measured by an NIR instrument, can be considered as a mixture of several constituents, the databases built by collecting actual samples bringing new information can reach hundreds, if not thousands, of samples.

Measurement of total soluble solids and moisture in puree and dry powder of Kakadu plum (Terminalia ferdinanadiana) samples using hand-held near infrared spectroscopy

2021 ◽  
pp. 096703352098236
Author(s):  
Eshetu M Bobasa ◽  
Michael E Netzel ◽  
Daniel Cozzolino ◽  
Anh Dao Thi Phan ◽  
Yasmina Sultanbawa
Keyword(s):  
Value Chain ◽  
Near Infrared ◽  
Cross Validation ◽  
Soluble Solids ◽  
Dry Powder ◽  
Aboriginal Communities ◽  
Total Soluble Solids ◽  
Fruit Samples ◽  
Kakadu Plum ◽  
Fruit Purée

Recent research has shown the potential of portable and handheld NIR instruments to monitor and measure the composition of fruits and vegetables. Current research has also shown the possibility of using portable instruments as tools to monitor composition along the entire food value chain. The objective of this study was to evaluate two sample presentation methods (dry powder and fruit puree) to measure total soluble solids (TSS) and moisture (M) in wild harvested Kakadu plum (KP) ( Terminalia ferdinandiana, Combretaceae). Kakadu plum is an endemic plant of Australia that contains high concentrations of vitamin C, ellagic acid as well as other bioactive compounds. These properties make this plant of high economic and social importance for the Aboriginal communities of Australia. Fruit samples were wild harvested in January 2020 from locations in the Kimberley region (Western Australia, Australia) and analysed using both reference and NIR spectroscopic methods. The SECV and RPD values in cross validation were 0.65% (RPD: 2.2) and 0.22% (RPD: 4.2) to predict M and TSS in the KP dry powder samples. The SECV and RPD values obtained in cross validation for the KP fruit puree samples were 0.56% (RPD: 2.8) and 0.24% (RPD: 3.8) for M and TSS, respectively. The results of this study demonstrated the ability of NIR spectroscopy to measure M and TSS in wild harvest fruit. These findings can be also utilised by the Aboriginal communities to develop a grading/sorting system to rapidly screen and evaluate relevant chemical parameters associated with fruit quality and safety.

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