Sugar Detection in Adulterated Honey via Fiber Optic Displacement Sensor for Food Industrial Applications

Bidin, Noriah; Zainuddin, Nurul Hida; Islam, Shumaila; Abdullah, Mundzir; Marsin, Faridah Mohd; Yasin, Moh.

doi:10.1109/jsen.2015.2479413

Cited by 34 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interestingly, the spectrum of pure honey is furthest away from the fructose-syrup-adulterated honey spectra. While the changes in the color of the honey may be subtle, as can be seen in the visible part of the spectrum, the observed spectral characteristics in the NIR region of the spectra suggest a change in other optical properties that may be connected to the scattering of light, for example, the refractive index [ 32 ], or the optical rotation, as was shown to be the case in other honey adulteration studies [ 33 , 34 , 35 ].…”

Section: Resultsmentioning

confidence: 87%

Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics

et al. 2022

View full text Add to dashboard Cite

Honey is a natural product that is considered globally one of the most widely important foods. Various studies on authenticity detection of honey have been fulfilled using visible and near-infrared (Vis-NIR) spectroscopy techniques. However, there are limited studies on stingless bee honey (SBH) despite the increase of market demand for this food product. The objective of this work was to present the potential of Vis-NIR absorbance spectroscopy for profiling, classifying, and quantifying the adulterated SBH. The SBH sample was mixed with various percentages (10–90%) of adulterants, including distilled water, apple cider vinegar, and high fructose syrup. The results showed that the region at 400–1100 nm that is related to the color and water properties of the samples was effective to discriminate and quantify the adulterated SBH. By applying the principal component analysis (PCA) on adulterants and honey samples, the PCA score plot revealed the classification of the adulterants and adulterated SBHs. A partial least squares regression (PLSR) model was developed to quantify the contamination level in the SBH samples. The general PLSR model with the highest coefficient of determination and lowest root means square error of cross-validation (RCV2=0.96 and RMSECV=5.88 %) was acquired. The aquaphotomics analysis of adulteration in SBH with the three adulterants utilizing the short-wavelength NIR region (800–1100 nm) was presented. The structural changes of SBH due to adulteration were described in terms of the changes in the water molecular matrix, and the aquagrams were used to visualize the results. It was revealed that the integration of NIR spectroscopy with aquaphotomics could be used to detect the water molecular structures in the adulterated SBH.

show abstract

Section: Resultsmentioning

confidence: 87%

Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics

et al. 2022

View full text Add to dashboard Cite

show abstract

“…When an individu has mild hypoglycemia, the limbic-striatal region of the brain will be activated and will create a strong appetite for high-calorie foods. Meanwhile euglikemia was more activate in medial prefrontal cortex that could lead to reduce food stimuli, in the other hands, high levels of glucose will brough out more powerful stimulation 14 . Thus, circulating glucose modulates stimulation regulation or barriers to food motivation.…”

Section: Discussionmentioning

confidence: 99%

Effect of Dietary Energy Density on Increasing Blood Glucose Pattern and Hunger-Satiety Sensation

2020

IJFMT

View full text Add to dashboard Cite

Background: There was no data how these food density could effect on glucose and visual analog levels based on hunger-filled scales in Indonesian population.Objective To determine the effect of food energy density on glucose levels and hunger-satiety sensation.Material and Methods: Seventeen women 18-22 years of age with BMI> 25, were given low energy density breakfast (n = 9) and high energy density (n = 7). The subjects were fasted for 10 hours, fasting blood glucose levels was measured as well as hunger-satiety sensation with a visual analog scale (VAS), prior to treatment. Breakfast was started at 08.00 a.m chew up to 32 times for 15 minutes. Blood glucose levels and VAS measurement were taken back in 2 hours and 4 hours afterward.Result: There was a significant differences in glucose levels between 4 hours postpandrial and 2 hours postpandrial (p = 0.031) and also between 4 hours postpandrial and fasting state (p = 0.042) in both groups. Analysis of hunger level VAS (p = 0.02) and satiety level VAS (0.04) at 4 hours postpandrial also differ significantly. Correlation analysis between hunger level VAS and blood glucose was different significantly (p = 0.01) with r = -0.59. Conclusions:The low energy density foods produce a pattern of increasing blood glucose levels were more stable, thus, more able to suppress the sensation of hunger than high energy density.

show abstract

“…There are many different methods proposed to detect honey adulteration include Stable Carbon Isotope Ratio Analysis (SCIRA) [10], Differential Scanning Calorimetry (DSC) [11], Surface Plasmon Resonance (SPR) [12], Nuclear Magnetic Resonance (NMR) [3], and Liquid Chromatography (LC) [9]. Other method includes High Performances Liquid Chromatography (HPLC)\ [4], [13], Fiber optic displacement sensor (FODS) [14], Electrical Impedance Spectroscopy [15], Electronic Tongue (ET) [16], Electronic Nose (EN) [10], and Near-Infrared Spectroscopy (NIRS) [11]. The aim of this research is to study on the different method to detect honey adulteration and the most common data analysis technique used for classification of honey adulteration.…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Honey Adulteration Combined with Multivariate Analysis

Zulkiflee¹,

Zoolfakar²,

Rani³

et al. 2022

IJIE

View full text Add to dashboard Cite

Honey is a natural sweetener with a yellowish substance made up of bee secretions and plant nectar extracts. Main composition of honey are sugars or carbohydrates and water in the chemical composition and contain a great number of minor components such as minerals, amino acids, proteins, acidity, and pH. Honey adulteration is a global concern due to lack of awareness of people and policies. There is a various method that has been conducted to detect honey adulteration such as SCIRA, DSC, FTIR, NIRS, and NMRand these methods mostly used multivariate data analysis to classify the adulteration of honey. PCA is the most used technique in the classification of honey adulteration where the data obtained is clustered according to adulteration level and type of theadulterant. This paper explains on different methods to detect honey adulteration and common technique used on classification of honey.It can be concluded that PCA is the most used technique based on different method of honey adulteration detection.

show abstract

Sugar Detection in Adulterated Honey via Fiber Optic Displacement Sensor for Food Industrial Applications

Cited by 34 publications

References 18 publications

Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics

Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics

Effect of Dietary Energy Density on Increasing Blood Glucose Pattern and Hunger-Satiety Sensation

Detection and Classification of Honey Adulteration Combined with Multivariate Analysis

Contact Info

Product

Resources

About