Correlation between near Infrared Spectra and Texture Profiling of Steam Cooked Potatoes

1998 ◽  
Vol 6 (A) ◽  
pp. A291-A297 ◽  
Author(s):  
C.G. Boeriu ◽  
D. Yuksel ◽  
R. van der Vuurst de Vries ◽  
T. Stolle-Smits ◽  
C. van Dijk
Keyword(s):  
Near Infrared ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quantitative Model ◽  
Matter Content ◽  
Dry Matter Content ◽  
Least Squares Regression ◽  
Sensory Data ◽  
Positive Side ◽  
Sensory Parameters

This study evaluated the ability of near infrared (NIR) spectrosocopy for the assessment of sensory texture properties of steam-cooked potatoes. The texture of steam-cooked potatoes from three cultivars, classified according to tuber size and dry matter content, was sensory evaluated after one, three and six months of storage. The sensory data were analysed using principal component analysis (PCA). PCA revealed that the first two principal components explained 95% or more of the variance between the data. The first principal component was dominated by the descriptors mealy and crumbly on the positive side and the descriptor waxy on the negative side. The descriptor firm had a high positive loading on the second principal component. For the same potato samples, NIR spectra were measured. A quantitative model based on partial least squares regression (PLS), relating NIR spectral data of fresh potato samples with sensory perceived texture attributes of same samples, has been developed. Among the sensory parameters, moist, waxy, firm and mealy were best predicted, with standard errors of calibration ( SEC) ranging from 7.4 to 10.5 and correlation coefficients (Rc) between 0.89 and 0.94.

Comparison of Transflectance and Reflectance to Analyse Hog Manures

2005 ◽  
Vol 13 (2) ◽  
pp. 99-107 ◽  
Author(s):  
W. Saeys ◽  
J. Xing ◽  
J. De Baerdemaeker ◽  
H. Ramon
Keyword(s):  
Near Infrared ◽  
Organic Matter Content ◽  
Nir Spectroscopy ◽  
Nutrient Content ◽  
Ammonium Nitrogen ◽  
Matter Content ◽  
Dry Matter Content ◽  
Least Squares Regression ◽  
Total N ◽  
Sample Presentation

In this study, the reflectance and transflectance sample presentation mode were compared for the analysis of the nutrient content of hog ( Sus domesticus) manure using visible and near infrared (vis-NIR) spectroscopy. A total of 194 hog manures, which were collected in the spring of 2004 from farms in the northern part of Belgium, were assayed by conventional wet chemical analysis and spectroscopy for the following constituents: dry matter content (DM), organic matter content (OM), pH, total Kjeldahl nitrogen (Ntot), ammonium nitrogen (NH4-N), phosphorus (P), potash (K), calcium (Ca), sodium (Na) and magnesium (Mg). Samples were scanned with a Foss NIRSystems Model 6500 scanning monochromator in reflectance and transflectance mode, respectively. A ceramic reference was measured in between the two modes. The monochromator was equipped with a DCFA sample presentation unit and ranges from 400 to 2498 nm. Partial least squares regression was employed to relate the spectral information to the nutrient content. The PLS models were calibrated for both sample presentation modes using leave-one-out cross-validation. The results of this study showed that the transflectance mode performed better than the reflectance mode. From the transflectance measurements, very good quantitative predictions for total N, good quantitative predictions for K, DM and OM, approximate predictions for NH4-N, P and Mg, very approximate predictions for Ca and a discrimination between high and low values for Na were obtained. pH was not predictable. The reflectance measurements were able to provide good quantitative predictions for total N and K, approximate quantitative predictions for NH4-N, very approximate predictions for DM, OM, P and Mg and discrimination between high and low values for Ca. Na was even less predictable and pH might be unpredictable.

scholarly journals Optimization of Instrument Design for In-Line Monitoring of Dry Matter Content in Single Potatoes by NIR Interaction Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 828
Author(s):  
Jens Petter Wold ◽  
Marion O’Farrell ◽  
Petter Vejle Andersen ◽  
Jon Tschudi
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Optical Measurement ◽  
Matter Content ◽  
Instrument Design ◽  
Calibration Model ◽  
Dry Matter Content ◽  
Least Squares Regression ◽  
Process Plant ◽  
Important Quality

Dry matter (DM) content is one of the most important quality features of potatoes. It defines the physical properties of the potatoes and determines what kind of product the potatoes can be used for. This paper presents the results obtained by a novel prototype NIR (near-infrared) instrument designed to measure DM content in single potatoes in process. The instrument is based on interaction measurements to measure deeper into the potatoes. It measures rapidly, up to 50 measurements per second, allowing several moving potatoes to be measured per second. The instrument also enables several interactance distances to be recorded for each measurement. The instrument was calibrated based on three different potato varieties and the calibration measurements were done in a process plant, making the calibration model suitable for in-line use. A good calibration for DM was obtained by partial least squares regression (RMSECV = 0.78% DM, R2 = 0.91). The instrument was tested in-line in the process plant and several batches of potatoes were monitored for the estimation of the DM distribution per batch. Accuracy of DM determination as function of measurement position on the potato was studied, and results indicate that NIR scans along the center part of the potatoes give slightly better results compared to scans taken on either side of the center. Small differences in optical measurement geometry influence the accuracy of the calibration models, underlining the importance of optimizing instrument design for successful measurements.

scholarly journals Low-cost, handheld near-infrared spectroscopy for root dry matter content prediction in cassava

2021 ◽  
Author(s):  
Jenna Hershberger ◽  
Edwige Gaby Nkouaya Mbanjo ◽  
Prasad Peteti ◽  
Andrew Smith Ikpan ◽  
Kayode Ogunpaimo ◽  
...  
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Low Cost ◽  
Model Performance ◽  
Matter Content ◽  
Dry Matter Content ◽  
Least Squares Regression ◽  
Breeding Programs ◽  
Test Sets ◽  
Root Dry Matter Content

Over 800 million people across the tropics rely on cassava as a major source of calories. While the root dry matter content (RDMC) of this starchy root crop is important for both producers and consumers, characterization of RDMC by traditional methods is time-consuming and laborious for breeding programs. Alternate phenotyping methods have been proposed but lack the accuracy, cost, or speed ultimately needed for cassava breeding programs. For this reason, we investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava. Oven-dried measurements of RDMC were paired with 21,044 scans of roots of 376 diverse clones from 10 field trials in Nigeria and grouped into training and test sets based on cross-validation schemes relevant to plant breeding programs. Mean partial least squares regression model performance ranged from R2p = 0.62 - 0.89 for within-trial predictions, which is within the range achieved with laboratory-grade spectrometers in previous studies. Relative to other factors, model performance was highly impacted by the inclusion of samples from the same environment in both the training and test sets. Random forest variable importance analysis of root spectra revealed increased importance in a region previously identified as predictive of water content in plants (~950 - 990 nm). With appropriate model calibration, the tested spectrometer will allow for field-based collection of spectral data with a smartphone for accurate RDMC prediction and potentially other quality traits, a step that could be easily integrated into existing harvesting workflows of cassava breeding programs.

scholarly journals Robustness of Tomato Quality Evaluation Using a Portable Vis-SWNIRS for Dry Matter and Colour

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
U. K. Acharya ◽  
P. P. Subedi ◽  
K. B. Walsh
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Short Wave ◽  
Matter Content ◽  
Soluble Solids ◽  
Practical Implementation ◽  
Calibration Model ◽  
Dry Matter Content ◽  
Least Squares Regression ◽  
Growing Conditions

The utility of a handheld visible-short wave near infrared spectrophotometer utilising an interactance optical geometry was assessed in context of the noninvasive determination of intact tomato dry matter content, as an index of final ripe soluble solids content, and colouration, as an index of maturation to guide a decision to harvest. Partial least squares regression model robustness was demonstrated through the use of populations of different harvest dates or growing conditions for calibration and prediction. Dry matter predictions of independent populations of fruit achievedR2ranging from 0.86 to 0.92 and bias from −0.14 to 0.03%. For a CIEa⁎colour model, predictionR2ranged from 0.85 to 0.96 and bias from −1.18 to −0.08. Updating the calibration model with new samples to extend range in the attribute of interest and in sample matrix is key to better prediction performance. The handheld spectrometry system is recommended for practical implementation in tomato cultivation.

Application of near Infrared Transmission Spectroscopy for the Determination of Some Quality Parameters of Apples

1994 ◽  
Vol 2 (4) ◽  
pp. 213-221 ◽  
Author(s):  
T. Lovász ◽  
P. Merész ◽  
A. Salgó
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Matter Content ◽  
Dry Matter Content ◽  
Quality Factors ◽  
Infrared Transmission ◽  
Transmission Method ◽  
Titratable Acid ◽  
Transmission Spectroscopy ◽  
Relative Standard

The acceptability of near infrared (NIR) transmission spectroscopy for the prediction of six quality factors of apples (firmness, refractive index, pH, titratable acid, dry matter and alcohol insoluble solids content) was investigated. The effects of storage conditions, cultivars and season on the accuracy of the NIR transmission method were also studied during the experiment. The accuracy of the calibration of all investigated parameters decreased during storage. The alteration of the characteristics of the spectra is possibly due to changes in the chemical composition and structure of apples between September and April. The calibration method was improved by developing a separate calibration for each cultivar per year. The calibrations of the different parameters are season-dependent except for the dry matter content. Using outlier diagnostics, the prediction accuracy can be generally improved by about 10%. The coefficient of variation for each parameter is compatible with the relative standard deviation for the reference methods except for the titratable acid content, showing the applicability of NIR transmission techniques. A relationship seems to exist between the maturity and the NIR transmission spectra of the apple.

scholarly journals Near-infrared Spectroscopy for Rapid Estimation of Dry Matter Content in Whole Unpeeled Potato Tubers

2012 ◽  
Vol 1 (4) ◽  
pp. 55 ◽  
Author(s):  
Trygve Helgerud ◽  
Vegard H. Segtnan ◽  
Jens P. Wold ◽  
Simon Ballance ◽  
Svein H. Knutsen ◽  
...  
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Reference Method ◽  
Quality Parameters ◽  
Matter Content ◽  
Dry Matter Content ◽  
Potato Tubers ◽  
External Reference ◽  
Explained Variance ◽  
Processed Potatoes

<p>The dry matter is one of the main quality parameters of raw and processed potatoes. In the present study, the potential of utilizing high throughput commercially available NIR interactance systems for dry matter determination in whole unpeeled potato tubers is investigated. The performance of a 2D NIR interactance instrument was compared with that of a 1D NIR interactance instrument and a traditional underwater weight apparatus. A total of 114 tubers were assessed individually with both of the NIR instruments (760-1040 nm), the underwater weight and an external reference method (freeze drying). The 1D interactance instrument obtained better prediction results than what the 2D instrument could achieve (R<sup>2</sup>=0.95, RMSECV=0.91, and R<sup>2</sup>=0.83, RMSECV=1.65, respectively). The underwater weight obtained the highest explained variance (R<sup>2</sup>=0.97), but the estimation was biased by approximately 1.5% (by weight). The poorer prediction performance of the 2D NIR interactance system can be partly explained by the lower penetration depths of the light compared to the 1D NIR interactance systems.</p>

Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds

2019 ◽  
Vol 59 (6) ◽  
pp. 1190 ◽  
Author(s):  
A. Bahri ◽  
S. Nawar ◽  
H. Selmi ◽  
M. Amraoui ◽  
H. Rouissi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Random Forest ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Principal Component ◽  
Least Squares Regression ◽  
Random Forest Regression ◽  
Milk Samples

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.

scholarly journals Cold storage of ‘Palmer’ mangoes sorted based on dry matter content using portable near infrared (VIS-NIR) spectrometer

2018 ◽  
Vol 42 (6) ◽  
pp. e13644 ◽  
Author(s):  
João Paixão dos Santos Neto ◽  
Gustavo Walace Pacheco Leite ◽  
Gabriele da Silva Oliveira ◽  
Luís Carlos Cunha Júnior ◽  
Priscila Lupino Gratão ◽  
...  
Keyword(s):  
Cold Storage ◽  
Dry Matter ◽  
Near Infrared ◽  
Matter Content ◽  
Dry Matter Content

Fungal decay of spruce and beech wood assessed by near-infrared spectroscopy in combination with uni- and multivariate data analysis

Holzforschung ◽  
2007 ◽  
Vol 61 (6) ◽  
pp. 680-687 ◽  
Author(s):  
Karin Fackler ◽  
Manfred Schwanninger ◽  
Cornelia Gradinger ◽  
Ewald Srebotnik ◽  
Barbara Hinterstoisser ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Lignin Content ◽  
Beech Wood ◽  
Multivariate Data ◽  
Principal Component ◽  
Brown Rot ◽  
White Rot ◽  
Least Squares Regression

Abstract Wood is colonised and degraded by a variety of micro-organisms, the most efficient ones are wood-rotting basidiomycetes. Microbial decay processes cause damage to wooden constructions, but also have great potential as biotechnological tools to change the properties of wood surfaces and of sound wood. Standard methods to evaluate changes in infected wood, e.g., EN350-1 1994, are time-consuming. Rapid FT-NIR spectroscopic methods are also suitable for this purpose. In this paper, degradation experiments on surfaces of spruce (Picea abies L. Karst) and beech (Fagus silvatica L.) were carried out with white rot basidiomycetes or the ascomycete Hypoxylon fragiforme. Experiments with brown rot or soft rot caused by Chaetomium globosum were also performed. FT-NIR spectra collected from the degraded wood were subjected to principal component analysis. The lignin content and mass loss of the specimens were estimated based on univariate or multivariate data analysis (partial least squares regression).

Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy

2002 ◽  
Vol 82 (4) ◽  
pp. 413-422 ◽  
Author(s):  
P D Martin ◽  
D F Malley ◽  
G. Manning ◽  
L. Fuller
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Analytical Data ◽  
Principal Component ◽  
Soil Samples ◽  
Organic N ◽  
Least Squares Regression ◽  
Total N ◽  
Organic C

This study explored the use of near-infrared spectroscopy (NIRS) for the rapid analysis of organic C (Corg) and organic N (Norg) in the A horizon of soil within a single field. Soil was sampled throughout a field in Manitoba, Canada to capture soil variability associated with topography. The soil samples were oven-dried and treated with acid to remove carbonates, after which C and N were determined by dry combustion. In this study, portions of the dried soil samples not treated with acid were scanned with a near-infrared scanning spectrophotometer between 1100 and 2500 nm. Correlating the spectral and the chemical analytical data using multiple linear regression or principal component analysis/partial least squares regression gave useful correlations for Corg. Over the range of 0–40 mg g-1 Corg, NIR-predicted values explained 75–78% of the variance in the chemical results. Results were improved to 80% for calibrations developed for the 0–20 mg g-1 organic C range. Useful results were not obtained for Norg although the literature shows that total N in soil is predictable using NIRS. It is likely that the acid treatment altered the composition of the samples in an inconsistent manner such that the chemically analyzed samples and those scanned by NIRS were different from each other in Norg concentration or composition. Extrapolation of these Corg results to the landscape scale implies that NIRS has potential to be a suitable method for mapping C for the purposes of monitoring C sequestration. Key words: Near-infrared spectroscopy, soil, carbon, nitrogen, topography, soil monitoring

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