Soluble Solids and Simple Sugars Measurement in Intact Mango Using Near Infrared Spectroscopy

A rapid, reliable, and nondestructive method for quality evaluation of mango (Magnifera indica) fruit is important to the mango industry for international trade. The objective of this study was to determine the potential of near-infrared (NIR) spectroscopy to predict soluble solids content (SSC) and individual and combined concentrations of sucrose, glucose, and fructose nondestructively in mango. Mature mangoes at two different temperatures (15 °C and 20 °C) were measured by NIR interactance (750–1088 nm wavelength region analyzed) over an 11-day period, starting when the fruit were underripe and extending to a few days past optimal ripeness. Partial least squares regression was used to develop models for SSC, individual sugar concentration, and the sum of the concentrations of the three sugars. Such analyses yielded calibration equations with R2 = 0.77 to 0.88 (SSC), 0.75 (sucrose), 0.67 (glucose), 0.70 (fructose), and 0.82 (sum); standard error of calibration = 0.56 to 0.90 (SSC), 10.0 (sucrose), 0.9 (glucose), 4.5 (fructose), and 10.4 (sum); and standard error of cross-validation = 0.93 to 1.10 (SSC), 15.6 (sucrose), 1.4 (glucose), 6.9 (fructose), and 16.8 (sum). When the SSC calibration was applied to a separate validation set, the standard error of performance ranged from 0.94% to 1.72%. These results suggest that for assessment of mango ripeness, NIR SSC calibrations are superior to the NIR calibrations for any of the individual sugars. This nondestructive technology can be used in the screening and grading of mangoes and in quality evaluation at wholesale and retail levels.

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Application of visible-near infrared spectroscopy to evaluate the quality of button mushrooms

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033518811921 ◽

2018 ◽

Vol 27 (1) ◽

pp. 38-45 ◽

Cited By ~ 2

Author(s):

Valentina Giovenzana ◽

Alessio Tugnolo ◽

Andrea Casson ◽

Riccardo Guidetti ◽

Roberto Beghi

Keyword(s):

Moisture Content ◽

Near Infrared ◽

Distribution Channel ◽

Nir Spectroscopy ◽

Quality Standard ◽

Quality Parameters ◽

Soluble Solids ◽

Least Squares Regression ◽

Physical Damage ◽

Reference Quality

The Agaricus bisporus mushroom is one of the most cultivated and consumed mushrooms in the world, thanks to its delicacy, nutritional value and flavour. The quality evaluation of the A. bisporus during the harvest is generally established by a visual check by trained operators. This method complies with the request of the Distribution Channel (DC) to retailers and guarantees very low physical damage to the mushrooms; nevertheless, it is subjective and it does not guarantee the highest quality standard for the consumer. The aim of this study was to test the use of visible/near infrared (vis/NIR) reflectance spectroscopy (400–1000 nm) to objectively evaluate the quality parameters of A. bisporus mushrooms. A total of 167 samples of A. bisporus mushrooms were harvested according to the main DC purchasing standards. The vis/NIR analyses were performed the day of sampling just before the physico-chemical analyses (sizes, firmness, soluble solids content and moisture content) used as reference quality parameters. The vis/NIR spectra were correlated to reference measures in order to build predictive models using the partial least squares regression method. Calculated models gave positive results regarding the prediction of the moisture content (r2(pred) = 0.78) and firmness (r2(pred) = 0.78). Results of this explorative study could be considered encouraging and demonstrate the applicability of vis/NIR spectroscopy on A. bisporus as a rapid technique (i) to monitor the productive process directly at the company, (ii) to standardize the harvest moment, and (iii) to support DC’s buyers’ choices, nowadays exclusively based on product external characteristics.

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

Foods ◽

10.3390/foods9040441 ◽

2020 ◽

Vol 9 (4) ◽

pp. 441 ◽

Cited By ~ 2

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.

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Evaluation of Carbohydrate Concentrations in Phalaenopsis Using Near-infrared Spectroscopy

Journal of the American Society for Horticultural Science ◽

10.21273/jashs04504-18 ◽

2018 ◽

Vol 143 (6) ◽

pp. 494-502

Author(s):

Yung-Kun Chuang ◽

I-Chang Yang ◽

Chao-Yin Tsai ◽

Jiunn-Yan Hou ◽

Yung-Huei Chang ◽

...

Keyword(s):

Standard Error ◽

Near Infrared ◽

Nir Spectroscopy ◽

Dry Weight ◽

Internal Quality ◽

Least Squares Regression ◽

First Derivative ◽

Derivative Spectra ◽

Calibration Models ◽

Quantitative Analyses

Carbohydrate concentrations are important indicators of the internal quality of Phalaenopsis. In this study, near-infrared (NIR) spectroscopy was used for quantitative analyses of fructose, glucose, sucrose, and starch in Phalaenopsis plants. Both modified partial least-squares regression (MPLSR) and stepwise multiple linear regression (SMLR) methods were used for spectral analysis of 302 Phalaenopsis samples in the full visible NIR wavelength range (400–2498 nm). Calibration models built by MPLSR were better than those built by SMLR. For fructose, the smoothed first derivative MPLSR model provided the best results, with a correlation coefficient of calibration (Rc) of 0.96, standard error of calibration (SEC) of 0.22% dry weight (DW), standard error of validation (SEV) of 0.28% DW, and bias of -0.01% DW. For glucose, the MPLSR model based on the smoothed first derivative spectra was the best (Rc = 0.96; SEC = 0.26% DW; SEV = 0.32% DW; and bias = 0.01% DW). The best MPLSR model of sucrose was developed using the smoothed first derivative spectra (Rc = 0.96; SEC = 0.24% DW; SEV = 0.31% DW; bias = -0.03% DW). Regarding starch, the smoothed first derivative MPLSR model showed the best effects (Rc = 0.91; SEC = 0.47% DW; SEV = 0.56% DW; bias = -0.02% DW). Both the MPLSR and SMLR models showed satisfactory predictability, indicating that NIR has the potential to be adopted as an effective method of rapid and accurate inspection of the carbohydrate concentrations of Phalaenopsis plants. This technique could contribute substantially to quality management of Phalaenopsis.

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Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

10.5194/egusphere-egu21-3896 ◽

2021 ◽

Author(s):

Iva Hrelja ◽

Ivana Šestak ◽

Igor Bogunović

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Spectral Data ◽

Near Infrared ◽

Mean Squared Error ◽

Nir Spectroscopy ◽

Soil Samples ◽

Least Squares Regression ◽

Macro Scale ◽

Sentinel 2

Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.The study is performed in Mediterranean Croatia (44&#176; 05&#8217; N; 15&#176; 22&#8217; E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed Quercus ssp. and Juniperus ssp. forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22nd 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31st and September 5th 2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSEp) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSEp (Spectroscopy dataset RMSEp = 1.91; Sentinel-2 dataset RMSEp = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.Keywords: remote sensing, soil spectroscopy, wildfires, soil organic matterAcknowledgment: This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Per&#269;in is acknowledged for her cooperation during the laboratory work.

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Ability of Vis-PIR spectroscopy to monitor changes in organic carbon of loamy soils at two depths

10.5194/egusphere-egu21-15021 ◽

2021 ◽

Author(s):

Hayfa Zayani ◽

Youssef Fouad ◽

Didier Michot ◽

Zeineb Kassouk ◽

Zohra Lili-Chabaane ◽

...

Keyword(s):

Organic Carbon ◽

Near Infrared ◽

Full Range ◽

Nir Spectroscopy ◽

Soil Samples ◽

Calibration Data ◽

Least Squares Regression ◽

Data Set ◽

Soil Carbon Stocks ◽

The Mean

Visible-Near Infrared (Vis-NIR) spectroscopy has proven its efficiency in predicting several soil properties such as soil organic carbon (SOC) content. In this preliminary study, we explored the ability of Vis-NIR to assess the temporal evolution of SOC content. Soil samples were collected in a watershed (ORE AgrHys), located in Brittany (Western France). Two sampling campaigns were carried out 5 years apart: in 2013, 198 soil samples were collected respectively at two depths (0-15 and 15-25 cm) over an area of 1200 ha including different land use and land cover; in 2018, 111 sampling points out of 198 of 2013 were selected and soil samples were collected from the same two depths. Whole samples were analyzed for their SOC content and were scanned for their reflectance spectrum. Spectral information was acquired from samples sieved at 2 mm fraction and oven dried at 40&#176;C, 24h prior to spectra acquisition, with a full range Vis-NIR spectroradiometer ASD Fieldspec&#174;3. Data set of 2013 was used to calibrate the SOC content prediction model by the mean of Partial Least Squares Regression (PLSR). Data set of 2018 was therefore used as test set. Our results showed that the variation &#8710;SOCobsobtained from observed values in 2013 and 2018 (&#8710;SOCobs = Observed SOC (2018) - Observed SOC (2013)) is ranging from 0.1 to 25.9 g/kg. Moreover, our results showed that the prediction performance of the calibrated model was improved by including 11 spectra of 2018 in the 2013 calibration data set (R&#178;= 0.87, RMSE = 5.1 g/kg and RPD = 1.92). Furthermore, the comparison of predicted and observed &#8710;SOC between 2018 and 2013 showed that 69% of the variations were of the same sign, either positive or negative. For the remaining 31%, the variations were of opposite signs but concerned mainly samples for which &#8710;SOCobs is less than 1,5 g/kg. These results reveal that Vis-NIR spectroscopy was potentially appropriate to detect variations of SOC content and are encouraging to further explore Vis-NIR spectroscopy to detect changes in soil carbon stocks.

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Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

Journal of Near Infrared Spectroscopy ◽

10.1177/09670335211057235 ◽

2022 ◽

pp. 096703352110572

Author(s):

Nicholas T Anderson ◽

Kerry B Walsh

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Short Wave ◽

Least Squares Regression ◽

Machine Learning Methods ◽

Transmission Geometry ◽

Modelling Techniques ◽

Test Sets ◽

Model Robustness

Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

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Single-Kernel FT-NIR Spectroscopy for Detecting Maturity of Cucumber Seeds Using a Multiclass Hierarchical Classification Strategy

Applied Sciences ◽

10.3390/app9235058 ◽

2019 ◽

Vol 9 (23) ◽

pp. 5058 ◽

Cited By ~ 1

Author(s):

Zeng ◽

Lü ◽

Qiu ◽

Lu ◽

Jiang

Keyword(s):

Near Infrared ◽

Seed Quality ◽

Hierarchical Classification ◽

Nir Spectroscopy ◽

Predictive Performance ◽

Nondestructive Method ◽

Maturation Stage ◽

Seed Industry ◽

Intrinsic Quality ◽

Classification Strategy

The maturity of seeds at harvest determines their intrinsic quality characteristics such as longevity and vigor, and these characteristics are dominant factors for seed quality evaluation in the seed industry. However, little information is available on how to identify and further classify the maturation stage of seeds in a way that is nondestructive, precise, rapid, and inexpensive, while also exactly meeting the need for the uniform control of seed performance in the seed industry to improve crop yield. This study demonstrated a nondestructive method for detecting seed maturity by using the single-kernel near-infrared spectroscopy (SK-NIRS) technique. The results showed that five classes of cucumber seeds with different maturation levels can be distinguished successfully. A tree-structured hierarchical classification strategy consisting of one soft independent modeling of class analogy (SIMCA) model and three partial least squares discriminant analysis (PLS-DA) models were proposed ending up with 99.69% of the overall classification accuracy and 0.9961 of Cohen’s kappa in the test set, and its predictive performance was superior to both SIMCA and PLS-DA for direct multiclass classification. SK-NIRS in combination with a multiclass hierarchical classification strategy was proved to be both intuitive and efficient in classifying cucumber seeds according to maturation levels.

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NIR Spectroscopy and Partial Least Squares Regression for the Determination of Phosphate Content and Viscosity Behaviour of Potato Starch

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.300 ◽

2001 ◽

Vol 9 (2) ◽

pp. 133-139 ◽

Cited By ~ 8

Author(s):

L.G. Thygesen ◽

S.B. Engelsen ◽

M.H. Madsen ◽

O.B. Sørensen

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Potato Starch ◽

Nir Spectroscopy ◽

Peak Time ◽

Phosphate Content ◽

Least Squares Regression ◽

Data Prediction

A set of 97 potato starch samples with a phosphate content corresponding to a phosphorus content between 0.029 and 0.11 g per 100 g dry matter was analysed using a Rapid Visco Analyzer (RVA) and near infrared (NIR) spectroscopy, (700–2498 nm). NIR-based prediction of phosphate content was possible with a root mean square error of cross-validation ( RMSECV) of 0.006% using PLSR (partial least squares regression). However, the NIR/PLSR model relied on weak spectral signals, and was highly sensitive to sample preparation. The best prediction of phosphate content from the RVA viscograms was a linear regression model based on the RVA variable Breakdown, which gave a RMSECV of 0.008%. NIR/PLSR prediction of the RVA variables Peak viscosity and Breakdown was successful, probably because they were highly related to phosphate content in the present data. Prediction of the other RVA variables from NIR/PLSR was mediocre (Through, Final Viscosity) or not possible (Setback, Peak time, Pasting temperature).

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Analysis of Black Wattle (Acacia mearnsii De Wild) Bark by Near Infrared Spectroscopy

Holzforschung ◽

10.1515/hf.2003.078 ◽

2003 ◽

Vol 57 (5) ◽

pp. 527-532 ◽

Cited By ~ 3

Author(s):

L. R. Schimleck ◽

Y. Yazaki

Keyword(s):

Near Infrared ◽

Rapid Assessment ◽

Nir Spectroscopy ◽

Hot Water ◽

Soluble Solids ◽

Tannin Content ◽

Acacia Mearnsii ◽

Considerable Potential ◽

Black Wattle

Summary The analysis of two sets of Acacia mearnsii De Wild (Black Wattle) samples by near infrared (NIR) spectroscopy is reported. Set 1 samples were characterised in terms of hot water extractives, Stiasny value and polyflavanoid content. Set 2 samples were characterised by nine different parameters, including tannin content. NIR spectra were obtained from the milled bark of all samples and calibrations developed for each parameter. Calibrations developed for hot water extractives and polyflavanoid content (set 1) gave very good coefficients of determination (R2) and performed well in prediction. Set 2 calibrations were generally good with total and soluble solids, tannin content, Stiasny value-2 and UV-2, all having R2 greater than 0.8. Owing to the small number of set 2 samples, no predictions were made using the calibrations. The strong relationships obtained for many parameters in this study indicates that NIR spectroscopy has considerable potential for the rapid assessment of the quality of extractives in A. mearnsii bark.

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Application of near-infrared spectroscopy for the detection of internal insect infestation inPiceaabiesseed lots

Canadian Journal of Forest Research ◽

10.1139/x03-189 ◽

2004 ◽

Vol 34 (1) ◽

pp. 76-84 ◽

Cited By ~ 17

Author(s):

Mulualem Tigabu ◽

Per Christer Odén ◽

Tong Yun Shen

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Least Squares Regression ◽

Absorption Bands ◽

Classification Rate ◽

Data Set ◽

Storage Lipids ◽

Nir Absorption ◽

Seed Origin ◽

Single Seeds

The use of near-infrared (NIR) spectroscopy to discriminate between uninfested seeds of Picea abies (L.) Karst and seeds infested with Plemeliella abietina Seitn (Hymenoptera, Torymidae) larva is sensitive to seed origin and year of collection. Five seed lots collected during different years from Sweden, Finland, and Belarus were used in this study. Initially, seeds were classified as infested or uninfested with X-radiography, and then, NIR spectra from single seeds were collected with a NIR spectrometer from 1100 to 2498 nm with a resolution of 2 nm. Discriminant models were derived by partial least squares regression using raw and orthogonal signal corrected spectra (OSC). The resulting OSC model developed on a pooled data set was more robust than the raw model and resulted in 100% classification accuracy. Once irrelevant spectral variations were removed by using OSC pretreatment, single-lot calibration models resulted in similar classification rates for the new samples irrespective of origin and year of collection. Dis criminant analyses performed with selected NIR absorption bands also gave nearly 100% classification rate for new samples. The origin of spectral differences between infested and uninfested seeds was attributed to storage lipids and proteins that were completely depleted in the former by the feeding larva.

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