A novel method for the age estimation of Saddletail snapper (Lutjanus malabaricus) using Fourier Transform-near infrared (FT-NIR) spectroscopy

Near infrared (NIR) spectroscopy was investigated as a potential rapid method of estimating fish age from whole otoliths of Saddletail snapper (Lutjanus malabaricus). Whole otoliths from 209 Saddletail snapper were extracted and the NIR spectral characteristics were acquired over a spectral range of 800–2780 nm. Partial least-squares models (PLS) were developed from the diffuse reflectance spectra and reference-validated age estimates (based on traditional sectioned otolith increments) to predict age for independent otolith samples. Predictive models developed for a specific season and geographical location performed poorly against a different season and geographical location. However, overall PLS regression statistics for predicting a combined population incorporating both geographic location and season variables were: coefficient of determination (R2) = 0.94, root mean square error of prediction (RMSEP) = 1.54 for age estimation, indicating that Saddletail age could be predicted within 1.5 increment counts. This level of accuracy suggests the method warrants further development for Saddletail snapper and may have potential for other fish species. A rapid method of fish age estimation could have the potential to reduce greatly both costs of time and materials in the assessment and management of commercial fisheries.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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Prediction of Fatty Acid Content in Beef Neck Lean by near Infrared Reflectance Analysis

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.141 ◽

1998 ◽

Vol 6 (1) ◽

pp. 229-234 ◽

Cited By ~ 27

Author(s):

William R. Windham ◽

W.H. Morrison

Keyword(s):

Fatty Acids ◽

Fatty Acid ◽

Near Infrared ◽

Fatty Acid Content ◽

Reference Method ◽

Nir Spectroscopy ◽

Coefficient Of Determination ◽

Longissimus Dorsi ◽

Near Infrared Reflectance ◽

Total Fatty Acids

Near infrared (NIR) spectroscopy in the prediction of individual and total fatty acids of bovine M. Longissimus dorsi neck muscles has been studied. Beef neck lean was collected from meat processing establishments using advanced meat recovery systems and hand-deboning. Samples ( n = 302) were analysed to determine fatty acid (FA) composition and scanned from 400 to 2498 nm. Total saturated and unsaturated FA values ranged from 43.2 to 62.0% and 38.3 to 56.2%, respectively. Results of partial least squares (PLS) modeling shown reasonably accurate models were attained for total saturate content [standard error of performance ( SEP = 1.10%); coefficient of determination on the validation set ( r2 = 0.77)], palmitic ( SEP = 0.94%; r2 = 0.69), unsaturate ( SEP = 1.13%; r2 = 0.77), and oleic ( SEP = 0.97; r2 = 0.78). Prediction of other individual saturated and unsaturated FAs was less accurate with an r2 range of 0.10 to 0.53. However, the sum of individual predicted saturated and unsaturated FA was acceptable compared with the reference method ( SEP = 1.10 and 1.12%, respectively). This study shows that NIR can be used to predict accurately total fatty acids in M. Longissimus dorsi muscle.

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Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy

Australian Journal of Experimental Agriculture ◽

10.1071/ea9910205 ◽

1991 ◽

Vol 31 (2) ◽

pp. 205 ◽

Cited By ~ 27

Author(s):

KF Smith ◽

PC Flinn

Keyword(s):

Nutritive Value ◽

Near Infrared ◽

Linear Regression Analysis ◽

Spectral Characteristics ◽

Nir Spectroscopy ◽

Cost Effective ◽

Multiple Linear Regression Analysis ◽

Infrared Reflectance ◽

Near Infrared Reflectance

Near infrared reflectance (NIR) spectroscopy is a rapid and cost-effective method for the measurement of organic constituents of agricultural products. NIR is widely used to measure feed quality around the world and is gaining acceptance in Australia. This study describes the development of an NIR calibration to measure crude protein (CP), predicted in vivo dry matter digestibility (IVDMD) and neutral detergent fibre (NDF) in temperate pasture species grown in south-western Victoria. A subset of 116 samples was selected on the basis of spectral characteristics from 461 pasture samples grown in 1987-89. Several grass and legume species were present in the population. Stepwise multiple linear regression analysis was used on the 116 samples to develop calibration equations with standard errors of 0.8,2.3 and 2.2% for CP, NDF and IVDMD, respectively. When these equations were tested on 2 independent pasture populations, a significant bias existed between NIR and reference values for 2 constituents in each population, indicating that the calibration samples did not adequately represent the new populations for these constituents. The results also showed that the H statistic alone was inadequate as an indicator of equation performance. It was confirmed that it was possible to develop a broad-based calibration to measure accurately the nutritive value of closed populations of temperate pasture species. For the resulting equations to be used for analysis of other populations, however, they must be monitored by comparing reference and NIR analyses on a small number of samples to check for the presence of bias or a significant increase in unexplained error.

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Application of near Infrared Spectroscopy On-Combine in Corn Grain Breeding

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.459 ◽

2005 ◽

Vol 13 (2) ◽

pp. 69-75 ◽

Cited By ~ 17

Author(s):

Roland Welle ◽

Willi Greten ◽

Thomas Müller ◽

Gary Weber ◽

Hartwig Wehrmann

Keyword(s):

Grain Yield ◽

Dry Matter ◽

Near Infrared ◽

Reference Method ◽

Nir Spectroscopy ◽

Coefficient Of Determination ◽

Successful Implementation ◽

Grain Moisture ◽

Yield Determination ◽

Corn Grain

Improving maize ( Zea mays L.) grain yield and agronomic properties are major goals for corn breeders in northern Europe. In order to facilitate field grain yield determination we measured corn grain moisture content with near infrared (NIR) spectroscopy directly on a harvesting machine. NIR spectroscopy, in combination with harvesting, significantly improved quality and speed of yield determination within the very narrow harvest time window. Moisture calibrations were developed with 2117 samples from the 2001 to 2003 crop seasons using six diode array spectrometers mounted on combines. These models were derived from databases containing spectra from all instruments. Spectrometer-specific calibrations cannot be used to predict samples measured on other instruments of the same type. Standard error of cross-validation ( SECV) and coefficient of determination ( R2) were 0.56 and 0.99%, respectively. Moisture standard errors of prediction ( SEPs) for the six instruments, using varying independent sample sets from the 2004 harvest, ranged between 0.59% and 0.99% with R2 values between 0.92 to 0.98. The six instruments produced the same dry matter predictions on a common sample set as indicated by high R2 and low biases among them, hence there was no need to apply specific standardisation algorithms. Moisture NIR spectroscopy determinations were significantly more precise than those obtained using the reference method. Analysis of variance revealed low least significant differences and high heritabilities. High precision and heritability demonstrate successful implementation of on-combine NIR spectroscopy for routine dry matter (yield) measurements.

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Predicting terpene content in dried conifer shoots using near infrared spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033520950516 ◽

2020 ◽

Vol 28 (5-6) ◽

pp. 308-314

Author(s):

Emilie Champagne ◽

Michaël Bonin ◽

Alejandro A Royo ◽

Jean-Pierre Tremblay ◽

Patricia Raymond

Keyword(s):

Gas Chromatography ◽

Picea Glauca ◽

Near Infrared ◽

Nir Spectroscopy ◽

Calibration Method ◽

Wide Distribution ◽

Coefficient Of Determination ◽

Calibration Model ◽

Freeze Dried ◽

Terpene Content

Terpenes are phytochemicals found in multiple plant genera, especially aromatic herbs and conifers. Terpene content quantification is costly and complex, requiring the extraction of oil content and gas chromatography analyses. Near infrared (NIR) spectroscopy could provide an alternative quantitative method, especially if calibration can be developed with the spectra of dried plant material, which are easier and faster to acquire than oil-based spectra. Here, multispecies NIR spectroscopy calibrations were developed for total terpene content (mono- and sesquiterpenes) and for specific terpenes (α-pinene, β-pinene and myrcene) with five conifers species ( Picea glauca, Picea rubens, Pinus resinosa, Pinus strobus and Thuja occidentalis). The terpene content of fresh shoot samples was quantified with gas chromatography. The NIR spectra were measured on freeze-dried samples (n = 137). Using a subset of the samples, modified partial least squares regressions of total terpene and the three individual terpenes content were generated as a functions of the NIR spectra. The standard errors of the internal cross-validations (values between 0.25 and 2.28) and the ratio of prediction to deviation ratios (RPD values between 2.20 and 2.38) indicate that all calibrations have similar accuracy. The independent validations, however, suggest that the calibrations for total terpene and α-pinene content are more accurate (respective coefficient of determination: r2 = 0.85 and 0.82). In contrast, calibrations for β-pinene and myrcene had a low accuracy (respectively: r2 = 0.62 and 0.08), potentially because of the low concentration of these terpenes in the species studied. The calibration model fits (i.e., r2) are comparable to previously published calibration using the spectra of dried shoot samples and demonstrate the potential of this method for terpenes in conifer samples. The calibration method used could be useful in several other domains (e.g. seedling breeding program, industrial), because of the wide distribution of terpenes and especially of pinenes.

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Rapid analysis of chemical composition in intact and milled rice cookies using near infrared spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033517726724 ◽

2017 ◽

Vol 25 (5) ◽

pp. 330-337 ◽

Cited By ~ 5

Author(s):

Latthika Wimonsiri ◽

Pitiporn Ritthiruangdej ◽

Sumaporn Kasemsumran ◽

Nantawan Therdthai ◽

Wasaporn Chanput ◽

...

Keyword(s):

Chemical Composition ◽

Near Infrared ◽

Wheat Gluten ◽

Nir Spectroscopy ◽

Rice Flour ◽

Coefficient Of Determination ◽

Gluten Free ◽

Least Squares Regression ◽

Milled Rice ◽

Control Programs

This study has investigated the potential of near infrared (NIR) spectroscopy to predict the content of moisture, protein, fat and gluten in rice cookies in different sample forms (intact and milled samples). Gluten-free (n = 48) and gluten (n = 48) rice cookies were formulated with brown and white rice flours in which butter was substituted with fat replacer at 0, 15, 30 and 45%. With regard to gluten cookies, rice flour was substituted with wheat gluten at 1, 3 and 5%. Partial least squares regression modeling produced models with coefficient of determination (R2) values greater than 0.88 from NIR spectra of intact samples and greater than 0.92 for milled samples. These models were able to predict the four components with a ratio of prediction to deviation greater than 2.7 and 3.8 in intact and milled samples, respectively. The results suggest that the models obtained from the intact samples can be successfully applied for chemical composition of rice cookies and are reliable enough use for potential quality control programs.

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Analytical tools to predict changes in properties of oriented strandboard exposed to the fungus Postia placenta

Holzforschung ◽

10.1515/hf.2006.053 ◽

2006 ◽

Vol 60 (3) ◽

pp. 332-338 ◽

Cited By ~ 3

Author(s):

Scott M. Kent ◽

Robert J. Leichti ◽

Jeffrey J. Morrell ◽

David V. Rosowsky ◽

Stephen S. Kelley

Keyword(s):

Weight Loss ◽

Shear Strength ◽

Specific Gravity ◽

Near Infrared ◽

Oriented Strand Board ◽

Nir Spectroscopy ◽

Coefficient Of Determination ◽

Multivariate Statistical ◽

X Ray ◽

Bearing Strength

Abstract Weight loss, specific gravity and strength are traditional measures of how wood changes after fungal exposure. This study investigated the effects of fungal decay on properties of oriented strand board (OSB) made of aspen including weight loss, specific gravity, dowel-bearing strength, shear strength, and alkali solubility. Shear strength and alkali solubility were strongly correlated with specific gravity. In addition, X-ray densitometry and near-infrared (NIR) spectroscopy were used to study the decay process. X-Ray densitometry was used to assess localized density around the dowel-bearing embedment zone of a nail. A statistical model using the specific gravity directly under the nail from dowel-bearing strength tests as the explanatory variable had a higher coefficient of determination than models using the gross specific gravity of the sample. Predictive models using NIR spectro-scopy, in combination with multivariate statistical methods, showed promise as predictors of weight loss, shear strength, dowel-bearing strength, and solubility.

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Overall precision test of near infrared spectroscopy on mango fruits (Mangifera indica CV. ‘Nam Dok Mai Si Thong’) by on-line and off-line systems

E3S Web of Conferences ◽

10.1051/e3sconf/202018704006 ◽

2020 ◽

Vol 187 ◽

pp. 04006

Author(s):

Wachiraya Lekhawattana ◽

Panmanas Sirisomboon

Keyword(s):

Near Infrared ◽

Mangifera Indica ◽

Nir Spectroscopy ◽

Soluble Solids ◽

Coefficient Of Determination ◽

Online Systems ◽

On Line ◽

Line Scanning ◽

Brix Value ◽

Precision Test

The near infrared (NIR) spectroscopy both on-line and off-line scanning was applied on mango fruits (Mangifera indica CV. ‘Nam dok mai- si Thong’) for the overall precision test. The reference parameter was total soluble solids content (Brix value). The results showed that the off-line scanning had a higher accuracy than on-line scanning. The scanning repeatability of the off-line and on-line systems were 0.00199 and 0.00993, respectively. The scanning reproducibility of the off-line and online systems were 0.00279 and 0.00513, respectively. The reference of measurement repeatability was 0.2. The maximum coefficient of determination (R2max) of the reference measurement was 0.894.

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Spectral reflectance indices sense desiccation induced changes in the thalli of Antarctic lichen Dermatocarpon polyphyllizum

Czech Polar Reports ◽

10.5817/cpr2018-2-21 ◽

2018 ◽

Vol 8 (2) ◽

pp. 249-259 ◽

Cited By ~ 2

Author(s):

Miloš Barták ◽

Kumud Bandhu Mishra ◽

Michaela Marečková

Keyword(s):

Spectral Reflectance ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Spectral Characteristics ◽

Structural Features ◽

Coefficient Of Determination ◽

Curvilinear Relationship ◽

Structural Deterioration ◽

Reflectance Indices

Lichens, in polar and alpine regions, pass through repetitive dehydration and rehydration events over the years. The harsh environmental conditions affect the plasticity of lichen’s functional and structural features for their survival, in a species-specific way, and, thus, their optical and spectral characteristics. For an understanding on how dehydration affects lichens spectral reflectance, we measured visible (VIS) and near infrared (NIR) reflectance spectra of Dermatocarpon polyphyllizum, a foliose lichen species, from James Ross Island (Antarctica), during gradual dehydration from fully wet (relative water content (RWC) = 100%) to dry state (RWC = 0%), under laboratory conditions, and compared several derived reflectance indices (RIs) to RWC. We found a curvilinear relationship between RWC and range of RIs: water index (WI), photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), modified chlorophyll absorption in reflectance indices (MCARI and MCARI1), simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and a new NIR shoulder region spectral ratio index (NSRI). The index NDVI was initially increased with maxima around 70% RWC and it steadily declined with further desiccation, whereas PRI in-creased with desiccation and steeply falls when RWC was below 10%. The curvilinear relationship, for RIs versus RWC, was best fitted by polynomial regressions of second or third degree, and it was found that RWC showed very high correlation with WI (R2 = 0.94) that is followed by MCARI (R2 = 0.87), NDVI (R2 = 0.83), and MCARI (R2 = 0.81). The index NSRI, proposed for accessing structural deterioration, was almost invariable during dehydration with the least value of the coefficient of determination (R2 = 0.28). This may mean that lichen, Dermatocarpon polyphyllizum, activates protection mechanisms initially in response to the progression of dehydration; however, severe dehydration causes deactivation of photosynthesis and associated pigments without much affecting its structure.

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

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033520947824 ◽

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.

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