scholarly journals Hyperspectral analysis for extraction of chemical characteristics in dehydrated bones

2017 ◽  
Author(s):  
Carolina Blanch-Perez-del-Notario ◽  
Andy Lambrechts
Keyword(s):  
Hyperspectral Imaging ◽  
Mineral Content ◽  
Spectral Response ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Chemical Characteristics ◽  
Connective Tissues ◽  
High Prediction

Gelatin, a valuable commodity in food processing, pharmaceuticals and photography, is produced by boiling the connective tissues, bones and skins of animals. To be able to predict the quality of the resulting gelatin, a number of parameters, such as percentage of fat, protein, water and mineral content, are measured in the raw bones. We evaluate in this paper whether hyperspectral imaging can perform the required fast and accurate prediction of these parameters based on the spectral response of bone samples. This would allow replacing the time-consuming chemical analysis. The spectral response of nine different bone batches in the 600–1000 nm range (Vis-NIR) is correlated by means of Partial Least Square regression with the measured parameters. Our results show that high prediction accuracy can be obtained for all measured parameters based on the Vis-NIR spectral response. We can then conclude that hyperspectral imaging is a promising metric for the estimation of these chemical characteristics.

Exploring the Vis-NIR wavelength importance in SOC models under field and lab conditions in a long-term field experiment

2021 ◽  
Author(s):  
Javier Reyes ◽  
Mareike Ließ
Keyword(s):  
Field Experiment ◽  
Spectral Response ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Field Conditions ◽  
Partial Least Square Regression ◽  
Preprocessing Technique ◽  
Term Field

<p>Soil organic carbon (SOC) is of particular interest in the study of agricultural systems as an indicator of soil quality and soil fertility. In the use of Vis-NIR spectroscopy for SOC detection, the interpretation of the spectral response with regards to the importance of individual wavelengths is challenging due to the soil’s composition of multiple organic and minerals compounds. Under field conditions, additional aspects affect the spectral data compared to lab conditions. This study compared the spectral wavelength importance in partial least square regression (PLSR) models for SOC between field and lab conditions. Surface soil samples were obtained from a long-term field experiment (LTE) with high SOC variability located in the state of Saxony-Anhalt, Germany. Data sets of Vis-NIR spectra were acquired in the lab and field using two spectrometers, respectively. Four different preprocessing methods were applied before building the models. Wavelength importance was observed using variable importance in projection. Differences in wavelength importance were observed depending on the measurement device, measurement condition, and preprocessing technique, although pattern matches were identifiable, especially in the NIR range. It is these pattern matches that aid model interpretation to effectively determine SOC under field conditions.</p>

scholarly journals Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

2019 ◽  
Vol 9 (21) ◽  
pp. 4587 ◽  
Author(s):  
Giuseppe Bonifazi ◽  
Giuseppe Capobianco ◽  
Silvia Serranti
Keyword(s):  
Hyperspectral Imaging ◽  
Spectral Response ◽  
Principal Component ◽  
Short Wave ◽  
Partial Least Square ◽  
Least Square ◽  
Construction And Demolition Waste ◽  
Infrared Range ◽  
Demolition Waste ◽  
Control Approach

Asbestos-Containing Materials (ACMs) are hazardous and prohibited to be sold or used as recycled materials. In the past, asbestos was widely used, together with cement, to produce “asbestos cement-based” products. During the recycling process of Construction and Demolition waste (C&DW), ACM must be collected and deposited separately from other wastes. One of the main aims of the recycling strategies applied to C&DW was thus to identify and separate ACM from C&DW (e.g., concrete and brick). However, to obtain a correct recovery of C&DW materials, control methodologies are necessary to evaluate the quality and the presence of harmful materials, such as ACM. HyperSpectral Imaging (HSI)-based sensing devices allow performing the full detection of materials constituting demolition waste. ACMs are, in fact, characterized by a spectral response that nakes them is different from the “simple” matrix of the material/s not embedding asbestos. The described HSI quality control approach is based on the utilization of a platform working in the short-wave infrared range (1000–2500 nm). The acquired hyperspectral images were analyzed by applying different chemometric methods: Principal Component Analysis for data exploration and hierarchical Partial Least-Square-Discriminant Analysis (PLS-DA) to build classification models. Following this approach, it was possible to set up a repeatable, reliable and efficient technique able to detect ACM presence inside a C&DW flow stream. Results showed that it is possible to discriminate and identify ACM inside C&DW. The recognition is potentially automatic, non-destructive and does not need any contact with the investigated products.

scholarly journals Estimation of strawberry firmness using hyperspectral imaging: a comparison of regression models

2021 ◽  
Author(s):  
Binu Devassy ◽  
Sony George
Keyword(s):  
Hyperspectral Imaging ◽  
Regression Models ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Regression Techniques ◽  
Important Quality ◽  
Non Destructive ◽  
Better Than

Firmness is one of the most important quality measures of strawberries, and is related to other aspects of the fruit, such as flavour, ripeness and internal characteristics. The most popular method for measuring firmness is puncturing with a penetrometer, which is destructive and time-consuming. In the present study, we make an attempt to predict the firmness of strawberries in a fast, non-destructive and non-contact way using hyperspectral imaging (HSI) and data analysis with various regression techniques. The primary goal of this research is to investigate and compare the firmness prediction capability of seven prominent regression techniques. We have performed HSI data acquisition of 150 strawberries and optimised seven regression models using the spectral information to predict strawberry firmness. These models are linear, ridge, lasso, k-neighbours, random forest, support vector and partial least square regression. The res ults show that HSI data with regression models has the potential to predict firmness in a rapid, non-destructive manner. Out of these seven regression models, the k-neighbours regression model outperformed all other methods with a standard error of prediction of 0.14, which is better than that of the state-of-the-art results.

scholarly journals PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 286
Author(s):  
Ofélia Anjos ◽  
Ilda Caldeira ◽  
Tiago A. Fernandes ◽  
Soraia Inês Pedro ◽  
Cláudia Vitória ◽  
...  
Keyword(s):  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Volatile Phenols ◽  
Near Ir ◽  
Liquid Liquid Extraction ◽  
Calibration Models ◽  
First Time

Near-infrared spectroscopic (NIR) technique was used, for the first time, to predict volatile phenols content, namely guaiacol, 4-methyl-guaiacol, eugenol, syringol, 4-methyl-syringol and 4-allyl-syringol, of aged wine spirits (AWS). This study aimed to develop calibration models for the volatile phenol’s quantification in AWS, by NIR, faster and without sample preparation. Partial least square regression (PLS-R) models were developed with NIR spectra in the near-IR region (12,500–4000 cm−1) and those obtained from GC-FID quantification after liquid-liquid extraction. In the PLS-R developed method, cross-validation with 50% of the samples along a validation test set with 50% of the remaining samples. The final calibration was performed with 100% of the data. PLS-R models with a good accuracy were obtained for guaiacol (r2 = 96.34; RPD = 5.23), 4-methyl-guaiacol (r2 = 96.1; RPD = 5.07), eugenol (r2 = 96.06; RPD = 5.04), syringol (r2 = 97.32; RPD = 6.11), 4-methyl-syringol (r2 = 95.79; RPD = 4.88) and 4-allyl-syringol (r2 = 95.97; RPD = 4.98). These results reveal that NIR is a valuable technique for the quality control of wine spirits and to predict the volatile phenols content, which contributes to the sensory quality of the spirit beverages.

scholarly journals Assessing the Quality of Milk Using a Multicomponent Analytical Platform MicroNIR/Chemometric

2020 ◽  
Vol 8 ◽  
Author(s):  
Roberta Risoluti ◽  
Giuseppina Gullifa ◽  
Stefano Materazi
Keyword(s):  
Prediction Models ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
The Novel ◽  
Screening Platform ◽  
Quality Of Milk ◽  
Different Origins ◽  
Non Destructive

In this work, an innovative screening platform based on MicroNIR and chemometrics is proposed for the on-site and contactless monitoring of the quality of milk using simultaneous multicomponent analysis. The novelty of this completely automated tool consists of a miniaturized NIR spectrometer operating in a wireless mode that allows samples to be processed in a rapid and accurate way and to obtain in a single click a comprehensive characterization of the chemical composition of milk. To optimize the platform, milk specimens with different origins and compositions were considered and prediction models were developed by chemometric analysis of the NIR spectra using Partial Least Square regression algorithms. Once calibrated, the platform was used to predict samples acquired in the market and validation was performed by comparing results of the novel platform with those obtained from the chromatographic analysis. Results demonstrated the ability of the platform to differentiate milk as a function of the distribution of fatty acids, providing a rapid and non-destructive method to assess the quality of milk and to avoid food adulteration.

Cultural intelligence of entrepreneurs and international network ties

2015 ◽  
Vol 38 (4) ◽  
pp. 421-436 ◽  
Author(s):  
Peerayuth Charoensukmongkol
Keyword(s):  
Cultural Intelligence ◽  
Export Performance ◽  
Small Sample ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Foreign Firms ◽  
Content Type ◽  
Foreign Competitors

Purpose – This paper aimed to investigate whether the cultural intelligence (CQ) of entrepreneurs is associated with the quality of the relationships firms develop with foreign networks. Design/methodology/approach – The samples include small and medium manufacturing firms in Thailand. Data were collected with a self-administered questionnaire survey. A list of 1,000 firms was randomly selected from the directory of Thai exporters. A total of 129 surveys were returned. Partial least square regression was used to analyze the data. Findings – The results revealed a positive association between the CQ of entrepreneurs and the quality of the relationships that small and medium enterprises (SMEs) had with foreign customers, foreign suppliers and foreign competitors. The quality of the relationships was also associated positively with export performance. However, there was no significant evidence for the role of the quality of relationships with foreign competitors in export performance. Research limitations/implications – The use of cross-sectional data makes it difficult to claim causality between the constructs. Moreover, the CQ and export performance measures that use subjective evaluation may cause bias. The small sample size also limits the generalizability of the results. Practical implications – The results suggested that CQ is a key capability entrepreneurs must develop to conduct business more successfully in foreign markets. Social implications – Because SMEs are considered a key driver of a country’s economic development, CQ training could be an important choice on which the government should focus. Furthermore, as the world economy is more integrated, CQ training can significantly help people improve cross-cultural communication skills which are essential for them to be successful in today’s globalized economy. Originality/value – Despite the increasing popularity of CQ research, evidence for its contribution to the ability of entrepreneurs to develop good relationships with foreign firms is lacking. The main contribution of this study is to bridge this research gap by providing empirical evidence.

scholarly journals Handheld Spectral Sensing Devices Should Not Mislead Consumers as Far as Non-Authentic Food Is Concerned: A Case Study with Adulteration of Milk Powder

Foods ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 75
Author(s):  
Thierry Delatour ◽  
Florian Becker ◽  
Julius Krause ◽  
Roman Romero ◽  
Robin Gruna ◽  
...  
Keyword(s):  
Milk Powder ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Handheld Devices ◽  
Portable Devices ◽  
Food Fraud ◽  
Start Up ◽  
Orthogonal Signal Correction

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5–10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.

Analysis of Total Polar Material in Selected Indian Snack’s Fried Oil

2021 ◽  
Vol 13 (2) ◽  
pp. 561-570
Author(s):  
R. Kumar ◽  
B. Bhattacharya ◽  
T. Agarwal ◽  
S. Chakkaravarthi
Keyword(s):  
Partial Least Square ◽  
Frying Oil ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Good Prediction ◽  
Polar Material ◽  
Total Polar Material ◽  
Prediction Capability ◽  
Fried Oil

The study was envisaged to examine the quality of frying oil used by street food vendors for two of the most popular food items viz. Samosa and Jalebi in India. Changes in the quality of frying oil were analysed by analysing the total polar material (TPM) content in the oil using an oil tester and Attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. Total 143 oil samples were collected at different frying times, i.e. 0, 2 and 4 h from five different Samosa and Jalebi vendors. In both the fried food oil samples, TPM content increased with increasing frying time. The TPM content in the 4 h fried oil samples of Jalebi was significantly (p< 0.001) higher than the samosa fried oil. Partial Least Square Regression (PLS) model based on the 1st derivative FTIR spectra exhibited good prediction capability for TPM values with a high regression coefficient (R2 ≥ 0.99) and low root mean square error (RMSE).

scholarly journals Determination and Quantification of the Distribution of CN-NL Nanoparticles Encapsulating Glycyrrhetic Acid on Novel Textile Surfaces with Hyperspectral Imaging

2020 ◽  
Vol 11 (2) ◽  
pp. 32 ◽  
Author(s):  
Kudirat A. Obisesan ◽  
Simona Neri ◽  
Elodie Bugnicourt ◽  
Inmaculada Campos ◽  
Laura Rodriguez-Turienzo
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Cross Validation ◽  
Imaging Techniques ◽  
Antioxidant Properties ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Glycyrrhetic Acid ◽  
Chemometric Tools

Chitin Lignin nanoparticles (CN-NL), standalone and encapsulating glycyrrhetic acid (GA), were applied on novel substrates for textiles to obtain antibacterial, antioxidant properties. Their homogeneous application is an important parameter that can strongly influence the final performance of the investigated textiles for its cosmetic and medical use. In this paper, hyperspectral imaging techniques combined with chemometric tools were investigated to study the distribution and quantification of CN-NL/GA on chitosan and CN-NL on pullulan substrates. To do so, samples of chitosan and pullulan impregnated with CN-NL/GA and CN-NL were analysed through Short Wave Infrared (SWIR) and Visible-Near Infrared (VisNIR) hyperspectral cameras. Two different chemometric tools for qualitative and quantitative analysis have been applied, principal component analysis (PCA) and partial least square regression (PLSR) models. Promising results were obtained in the VisNIR range, which made it possible for us to visualize the CN-NL/GA compound on chitosan and CN-NL on pullulan substrates. Additionally, the PLSR model results had determination coefficient ( R C 2 ) for calibration and cross-validation ( R C V 2 )   values of 0.983 and 0.857, respectively. Minimum values of root-mean-square error for calibration (RMSEC) and cross-validation (RMSECV) of CN-NL/GA were 0.333 and 0.993 g, respectively. The results demonstrate that hyperspectral imaging combined with chemometrics offers a powerful tool for studying the distribution on chitosan and pullulan substrates and to quantify the content of CN-NL/GA compounds on chitosan substrates.

scholarly journals Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 154 ◽  
Author(s):  
Hongzhe Jiang ◽  
Fengna Cheng ◽  
Minghong Shi
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Limit Of Detection ◽  
Principal Component ◽  
Rapid Identification ◽  
Performance Comparison ◽  
Correlation Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression

Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with Rp2 = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that Rp2 was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.

Sign in / Sign up

Export Citation Format

Share Document