scholarly journals The comprehensive quality evaluation of scutellariae radix based on HPLC fingerprint and antibacterial activity

2021 ◽  
Vol 336 ◽  
pp. 06025
Author(s):  
Yan Gao ◽  
Longfei Yang ◽  
Xiaoyan Ding ◽  
Bianli Wang ◽  
Longhui Zu ◽  
...  
Keyword(s):  
Antibacterial Activity ◽  
Biological Activities ◽  
Shandong Province ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Hplc Fingerprint ◽  
Scutellariae Radix ◽  
Different Origins

Scutellaria Radix, a traditional Chinese medicine, studies on its main active ingredient are limited. In this study, the purpose was to investigate the quality difference of Scutellariae Radix from different origins based on chemical components and biological activities. The chromatographic fingerprints of Scutellariae Radix from 33 origins were established using HPLC, and the antibacterial activities were studied with the microdilution method. Moreover, orthogonal partial least-square regression, pearson correlational analysis and grey relational analysis methods were performed to explore the relationship between the compositions and bioactivities. In addition, and origin identification model was established to comprehensively evaluate the quality of Scutellariae Radix. The results showed that Scutellariae Radix had in-vitro antibacterial effect on Staphylococcus aureus, and the best were in Gansu, Shandong Province. Multivariate statistical analysis common showed that three components were positively correlated with antibacterial activity, which were respectively wogonin, baicalein and oroxylin. In conclusion, the quality of Scutellariae Radix varies greatly from different origins, and the better was in Gansu and Shandong Province. This work successfully provides a general model that combined the chromatographic fingerprint and bioactivity assay to study the spectrum–effect relationships, which could be used to discover the primary active ingredients in traditional Chinese medicines.

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.

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.

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).

Internal Quality Estimation of Watermelon by Multiple Acoustic Signal Sensing

2006 ◽  
Vol 321-323 ◽  
pp. 1209-1212 ◽  
Author(s):  
Kang Jin Lee ◽  
Wank Yu Choi ◽  
Gi Young Kim ◽  
Suk Won Kang ◽  
Sang Ha Noh
Keyword(s):  
Evaluation System ◽  
Noise Removal ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Internal Quality ◽  
Internal Defect ◽  
Sorting System ◽  
Non Destructive

Watermelons are usually sorted by theirs weight and internal quality. Some automated watermelon weight sorters have been developed and operated in watermelon production areas. However, inspection of internal quality of watermelon is still performed by manually. Principal method of identifying internal defect of watermelon is analyzing the percussion sound of watermelon by human experts. Development of non-destructive evaluation technique for internal quality of watermelon is required to reduce human decision errors. The objective of this study was to develop a non-destructive sorting system which can detect internal defect of watermelons. The internal defect evaluation system has a constant-force hitting hammer to generate the acoustic sound, a multi-point sound signal acquiring system, a noise removal circuit, and a signal processing and quality evaluation program. An internal quality prediction model by PLSR (Partial Least Square Regression) was developed by analyzing the percussion sound of watermelons. Using the developed model, the prediction result shows that the overall prediction accuracy was 90.1%, and severely defected watermelons were identified perfectly.

scholarly journals Using Optimized Two and Three-Band Spectral Indices and Multivariate Models to Assess Some Water Quality Indicators of Qaroun Lake in Egypt

2021 ◽  
Vol 13 (18) ◽  
pp. 10408
Author(s):  
Salah Elsayed ◽  
Mohamed Gad ◽  
Mohamed Farouk ◽  
Ali H. Saleh ◽  
Hend Hussein ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Quality Indicators ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Water Quality Indicators ◽  
Chl A ◽  
Strong Relationships

Standard methods are limited for monitoring and managing water quality indicators (WQIs) in real-time and on a large scale. Consequently, there is an urgent need to use reliable, practical, swift, and cost-effective monitoring tools that can be easily deployed and assist decision makers in assessing key indicators relevant to surface water quality in a comprehensive manner. Surface water samples were collected and evaluated for water quality at 16 distinct sites across the Qaroun Lake in 2018 and 2019. Different WQIs, including total dissolved solids (TDS), transparency, total suspended solids (TSS), chlorophyll-a (Chl-a), and total phosphorus (TP), were tested for aquatic utilization. An integrated approach comprising WQIs, geospatial techniques, hyperspectral reflectance indices (SRIs) (commonly used SRIs, two-band and three-band SRIs (Spectral index calculated from water spectral reflectance of two or three wavelengths)), and partial least square regression (PLSR) models were used to assess the water quality of Qaroun Lake. According to the findings, the water quality attributes are polluted to varying degrees. The majority of commonly used SRIs presented moderately relationship with four WQIs (transparency, TSS, Chl-a, and TP) (R2 = 0.45 to 0.64), while the majority of newly two-band SRIs (NSRIs-2b) indicated moderate to strong relationships with WQIs (R2 = 0.51 to 0.74), and the majority of newly three band SRIs (NSRIs-3b) presented strong relationships with WQIs (R2 = 0.67 to 0.81). Broadly, the highest coefficients of determination were noticed with the NSRIs-3b followed by the NSRIs-2b and then the commonly used SRIs. For example, the NSRIs-3b (NDSI648,712,696) had stronger relationships with transparency, TSS, and Chl-a with R2 = 0.77, 0.66, and 0.81, respectively, than other SRIs. In addition, the NSRIs-3b (NDSI620,610,622) showed the highest R2 of 0.73 with TSS. The NSRIs-3b coupling with PLSR predicted the WQIs with satisfactory accuracy in the calibration (reach up R2 = 0.85) and validation (reach up R2 = 0.81) datasets. The overall findings of this research study showed that deriving an optimized NSRIs-3b from spectrum region and combining it with PLSR model could be a practical tool for managing water quality of the Qaroun Lake by accurately, timely, and non-destructively monitoring the WQIs.

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.

scholarly journals MODEL PENGEMBANGAN PERILAKU IBU DALAM PEMENUHAN KEBUTUHAN ASAH, ASIH DAN ASUH ANAK LEUKEMIA

Jurnal NERS ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. 63
Author(s):  
Praba Diyan Rachmawati ◽  
Reza Ranuh ◽  
Yuni Arief
Keyword(s):  
Quality Of Life ◽  
Self Efficacy ◽  
Basic Needs ◽  
Poor Quality ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Regression Result ◽  
Centered Care

Introduction: Children with leukemia have a poor quality of  live. A long term periode of care, treatment, side effect of treatment and the symptom of the leukemia disease will have an impact on leukemia children quality of life. Parents have an important role in meeting the basic needs of the child by stimulation, emotion and physical. With the fulfillment of the basic needs of children, the quality of life of children with leukemia will increase. Method: Design used in this study was an observational analytic in Hematology room Dr. Soetomo Hospital. The population was mother of leukemia children Bona 1 hematology room in Dr Soetomo Hospital. Conducted on 17th April- 17th May 2014. Consecutive sampling was used in this study. Sample were 20 mother who met in inclusion criteria. Independent variables were mother’s characteristics, self efficacy source, family centre care, Parental Self efficacy and dependent variables were mother’s behaviour. Data was collected using quetionnaire and analyzed using PLS (Partial Least Square) Regression. Result: Result showed that sources of self-efficacy influence on self-efficacy, self efficacy and mother’s characteristics directly affects the mother’s behaviour. Family centered care can’t directly affect mother’s behaviour. Discussion: It can be concluded that mother’s behaviour model in stimulating, loving and physical caring children with leukemia can be formed from mother’s characteristics, self efficacy source and Parental self efficacy. Parental self efficacy can be increased especially by improving coping mechanisms and vicarious experience. 

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

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