scholarly journals Evaluation of the physicochemical content and solid-state fermentation stage of Zhenjiang aromatic vinegar using near-infrared spectroscopy

2020 ◽  
Vol 16 (12) ◽  
Author(s):  
Suwan Fan ◽  
Tianhong Pan ◽  
Guoquan Li

AbstractAs one of the most famous traditional Chinese vinegars, the grains physicochemical content of Zhenjiang aromatic vinegar during solid-state fermentation (SSF) reflects the growth status of microorganisms and the quality of fermentation products. In addition, the time for grain-turning has a significant effect on the quality of fermentation products. In this study, a new evaluation method combined near-infrared (NIR) spectroscopy with partial least squares regression (PLSR) was proposed to predict the physicochemical content of grains and the fermentation stage. The performance of the PLSR models for the total acid and the nonvolatile acid were RMSEP = 0.0371, Rp2 = 0.9760, and RMSEP = 0.0216, Rp2 = 0.9646, respectively. The accuracy ratio of SSF stage judgment was 100%. Experimental results indicate that the proposed method can be used to guide on-site grain-turning and improve the quality of fermentation products.

2022 ◽  
pp. 096703352110572
Author(s):  
Nicholas T Anderson ◽  
Kerry B Walsh

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.


PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1416 ◽  
Author(s):  
Jishan Chen ◽  
Ruifen Zhu ◽  
Ruixuan Xu ◽  
Wenjun Zhang ◽  
Yue Shen ◽  
...  

Due to a boom in the dairy industry in Northeast China, the hay industry has been developing rapidly. Thus, it is very important to evaluate the hay quality with a rapid and accurate method. In this research, a novel technique that combines near infrared spectroscopy (NIRs) with three different statistical analyses (MLR, PCR and PLS) was used to predict the chemical quality of sheepgrass (Leymus chinensis) in Heilongjiang Province, China including the concentrations of crude protein (CP), acid detergent fiber (ADF), and neutral detergent fiber (NDF). Firstly, the linear partial least squares regression (PLS) was performed on the spectra and the predictions were compared to those with laboratory-based recorded spectra. Then, the MLR evaluation method for CP has a potential to be used for industry requirements, as it needs less sophisticated and cheaper instrumentation using only a few wavelengths. Results show that in terms of CP, ADF and NDF, (i) the prediction accuracy in terms of CP, ADF and NDF using PLS was obviously improved compared to the PCR algorithm, and comparable or even better than results generated using the MLR algorithm; (ii) the predictions were worse compared to laboratory-based spectra with the MLR algorithmin, and poor predictions were obtained (R2, 0.62, RPD, 0.9) using MLR in terms of NDF; (iii) a satisfactory accuracy with R2 and RPD by PLS method of 0.91, 3.2 for CP, 0.89, 3.1 for ADF and 0.88, 3.0 for NDF, respectively, was obtained. Our results highlight the use of the combined NIRs-PLS method could be applied as a valuable technique to rapidly and accurately evaluate the quality of sheepgrass hay.


Author(s):  
Krzysztof Wójcicki

The objective of the research study was to apply near infrared (NIR) spectroscopy to evaluate the quality of protein supplements available in the Polish shops and gyms. The evaluation was performed on the basis of the determination of the protein quantity contained in the individual samples by a Kjeldahl method and then the evaluation results were correlated with the measured NIR spectra using an appropriate chemometric method. The research material consisted of fifteen protein supplement samples for athletes, which included the following types: WPI (protein isolate), WPC (protein concentrate), WPH (protein hydrolysate), and mixtures thereof. The obtained NIR spectra of protein supplements were characterized by a similar shape of the bands. Depending on the type of protein, a different intensity of absorption of individual bands could be observed. A Principal Component Analysis (PCA) was used to distinguish the samples based on the spectra measured. Unfortunately, owing to the varying composition of the protein mixtures, it was not possible to find characteristic arrangement of the samples depending on their types. The spectra were correlated with the protein contents determined in the samples using a Partial Least Squares regression method (PLS regression) and various mathematic transformations of the NIR spectral data. The obtained regression models were analysed and the analysis results confirmed that it was possible to apply NIR spectra to estimate the content of proteins in protein supplements. The best result was obtained in a spectrum region between 9401 and 5448 cm-1 and after the first derivative was applied with Multiplicate Scatter Correction (MSC) as a mathematical pre-treatment. On the basis of the results obtained, it was proved that the NIR spectra applied together with the chemometric analysis could be used to quickly evaluate the products studied.


2008 ◽  
Vol 16 (5) ◽  
pp. 445-454 ◽  
Author(s):  
Patrícia Baptista ◽  
Pedro Felizardo ◽  
José C. Menezes ◽  
M. Joana Neiva Correia

Biodiesel is a mixture of fatty acid methyl esters, derived from vegetable oils or animal fats, which is usually produced by a transesterification reaction, where the oils or fats react with an alcohol in the presence of a catalyst. The quality of the oils used for biodiesel production strongly influences the final properties of biodiesel, namely its compliance to the European Standard. This work reports the use of near infrared (NIR) spectroscopy in the quality control of several oil properties, such as the iodine value, the water content and the acid number but, more importantly, the weight–weight percentages (wt%) of soybean, palm and rapeseed oil in mixtures. Principal component analysis was used to perform a qualitative analysis of the spectra, whereas partial least squares regression allowed the development of calibration models between analytical reference data and NIR spectra. The calibration ranges were 60–126 g I2 100 g−1 for the iodine value, 478–2500 mg kg−1 for the water content and 0.13-6.56 mg KOH g−1 for the acid number, whereas the validation errors were around 3.1 g I2 100 g−1, 111 mg kg−1 and 0.22 mg KOH g−1, respectively. The results obtained show that NIR spectroscopy is a promising technique to carry out the quality control of the commonly used vegetable oils for biodiesel production, namely the quality assurance and authenticity. Furthermore, it is of great value to have a simple, fast and reliable method to identify the composition of an oil mixture and/or some of its quality parameters, prior to storage or upon admission of a new lot of oil.


2014 ◽  
Vol 07 (06) ◽  
pp. 1450003 ◽  
Author(s):  
Ravipat Lapcharoensuk ◽  
Panmanas Sirisomboon

The goal of this research was to study the relationship between the eating quality of cooked rice and near infrared spectra measured by a Fourier Transform near infrared (FT–NIR) Spectrometer. Samples of milled: parboiled rice, white rice, new Jasmine rice (harvested in 2012) and aged Jasmine rice (harvested in 2006 or during the period 2007–2011) were used in this study. The eating quality of the cooked rice, i.e., adhesiveness, hardness, dryness, whiteness and aroma, were evaluated by trained sensory panelists. FT–NIR spectroscopy models for predicting the eating quality of cooked rice were established using the partial least squares regression. Among the eating quality, the stickiness model indicated its highest prediction ability (i.e., [Formula: see text]; RMSEP = 0.65; Bias = 0.00; RPD = 1.87) and SEP/SD of 2. In addition, it was clear that the water content did not affect the eating quality of cooked rice, rather the main chemical component implicated was starch.


Recycling ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Kirsti Cura ◽  
Niko Rintala ◽  
Taina Kamppuri ◽  
Eetta Saarimäki ◽  
Pirjo Heikkilä

In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.


2021 ◽  
Author(s):  
Iva Hrelja ◽  
Ivana Šestak ◽  
Igor Bogunović

<p>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.</p><p>The study is performed in Mediterranean Croatia (44° 05’ N; 15° 22’ E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed <em>Quercus ssp.</em> and <em>Juniperus ssp.</em> forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22<sup>nd</sup> 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 31<sup>st</sup> and September 5<sup>th </sup>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 (RMSE<sub>p</sub>) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.</p><p>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 RMSE<sub>p </sub>(Spectroscopy dataset RMSE<sub>p</sub> = 1.91; Sentinel-2 dataset RMSE<sub>p</sub> = 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.</p><p><strong>Keywords:</strong> remote sensing, soil spectroscopy, wildfires, soil organic matter</p><p><strong>Acknowledgment: </strong>This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Perčin is acknowledged for her cooperation during the laboratory work.</p>


1997 ◽  
Vol 20 (5) ◽  
pp. 285-290 ◽  
Author(s):  
U.A. Müller ◽  
B. Mertes ◽  
C. Fischbacher ◽  
K.U. Jageman ◽  
K. Danzer

The feasibility of using near infrared reflection spectroscopy for non-invasive blood glucose monitoring is discussed. Spectra were obtained using a diode-array spectrometer with a fiberoptic measuring head with a wavelength ranging from 800 nm to 1350 nm. Calibration was performed using partial least-squares regression and radial basis function networks. The results of different methods used to evaluate the quality of the recorded spectra in order to improve the reliability of the calibration models, are presented.


Author(s):  
Honghui Shi ◽  
Endian Yang ◽  
Yun Li ◽  
Xiaoyang Chen ◽  
Junjie Zhang

The drumstick tree is a fast-growing multipurpose tree with a large biomass and high nutritional value. However, it has rarely been exploited as a protein source. This study investigated solid-state fermentation induced by Aspergillus niger, Candida utilis and Bacillus subtilis to obtain high-quality protein feed from drumstick leaf flour. The results showed that fermentation induced significant changes in the nutritional composition of drumstick leaf flour. The concentrations of crude protein, small peptides and amino acids increased significantly after fermentation. The protein profile was also affected by the fermentation process. Macromolecular proteins in drumstick leaf flour were degraded, whereas other high molecular weight proteins were increased. However, the concentrations of crude fat, fiber, total sugar and reducing sugar were decreased, as were the anti-nutritional factors tannins, phytic acid and glucosinolates. After 24 h fermentation, the concentrations of total phenolics and flavonoids were increased. The antioxidant capacity was also significantly enhanced.


2021 ◽  
Author(s):  
Hayfa Zayani ◽  
Youssef Fouad ◽  
Didier Michot ◽  
Zeineb Kassouk ◽  
Zohra Lili-Chabaane ◽  
...  

<p>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°C, 24h prior to spectra acquisition, with a full range Vis-NIR spectroradiometer ASD Fieldspec®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 ∆SOC<sub>obs</sub><sub></sub>obtained from observed values in 2013 and 2018 (∆SOC<sub>obs</sub> = 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 (R²= 0.87, RMSE = 5.1 g/kg and RPD = 1.92). Furthermore, the comparison of predicted and observed ∆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 ∆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.</p>


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