Botanical origin identification and adulteration quantification of honey based on Raman spectroscopy combined with convolutional neural network

Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee

(2022) Swathi Sirisha Nallan Chakravartula et al.   FOOD CONTROL

Identification of olive oil in vegetable blend oil by one-dimensional convolutional neural network combined with Raman spectroscopy

(2022) Xijun Wu et al.   JOURNAL OF FOOD COMPOSITION AND ANALYSIS

New Zealand honey botanical origin classification with hyperspectral imaging

(2022) Guyang Zhang et al.   JOURNAL OF FOOD COMPOSITION AND ANALYSIS

Identification and quantification of adulterated honey by Raman spectroscopy combined with convolutional neural network and chemometrics

(2022) Xijun Wu et al.   SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network

(2021) R.R. Pullanagari et al.   REMOTE SENSING OF ENVIRONMENT

Detection of acacia honey adulteration with high fructose corn syrup through determination of targeted α‑Dicarbonyl compound using ion mobility-mass spectrometry coupled with UHPLC-MS/MS

(2021) Sha Yan et al.   FOOD CHEMISTRY

Detection of honey adulteration – The potential of UV-VIS and NIR spectroscopy coupled with multivariate analysis

(2021) Davor Valinger et al.   LWT-FOOD SCIENCE AND TECHNOLOGY

Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices

(2021) Yao Liu et al.   TRENDS IN FOOD SCIENCE & TECHNOLOGY

Honey authentication in terms of its adulteration with sugar syrups using UV–Vis spectroscopy and one-class classifiers

(2021) Rayara Ribeiro de Souza et al.   FOOD CHEMISTRY

Deep learning in analytical chemistry

(2021) Bruno Debus et al.   TRAC-TRENDS IN ANALYTICAL CHEMISTRY

Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering

(2020) Félix Lussier et al.   TRAC-TRENDS IN ANALYTICAL CHEMISTRY

Classification of honey applying high performance liquid chromatography, near-infrared spectroscopy and chemometrics

(2020) Shima Ghanavati Nasab et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Tutorial: multivariate classification for vibrational spectroscopy in biological samples

(2020) Camilo L. M. Morais et al.   Nature Protocols

Detection of adulteration in Chinese honey using NIR and ATR-FTIR spectral data fusion

(2020) Furong Huang et al.   SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

A laser-induced breakdown spectroscopy multi-component quantitative analytical method based on a deep convolutional neural network

(2020) Lu-Ning Li et al.   SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY

Laser induced breakdown spectroscopy for elemental analysis and discrimination of honey samples

(2020) Dimitrios Stefas et al.   SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY

Raman spectroscopy coupled with chemometrics for food authentication: A review

(2020) Yi Xu et al.   TRAC-TRENDS IN ANALYTICAL CHEMISTRY

Chemometric non-targeted analysis for detection of soybean meal adulteration by near infrared spectroscopy

(2020) O.Ye Rodionova et al.   FOOD CONTROL

Current trends and recent advances on food authenticity technologies and chemometric approaches

(2019) Sonia Medina et al.   TRENDS IN FOOD SCIENCE & TECHNOLOGY

Standardization of complex biologically derived spectrochemical datasets

(2019) Camilo L. M. Morais et al.   Nature Protocols

Detection techniques for adulterants in honey: Challenges and recent trends

(2019) Kuan Wei Se et al.   JOURNAL OF FOOD COMPOSITION AND ANALYSIS

A screening method based on Visible-NIR spectroscopy for the identification and quantification of different adulterants in high-quality honey

(2019) Ma José Aliaño-González et al.   TALANTA

Feature visualization of Raman spectrum analysis with deep convolutional neural network

(2019) Masashi Fukuhara et al.   ANALYTICA CHIMICA ACTA

Evaluation of honey in terms of quality and authenticity based on the general physicochemical pattern, major sugar composition and δ13C signature

(2019) Elisabeta-Irina Geană et al.   FOOD CONTROL

Detection of adulteration in Chinese monofloral honey using 1H nuclear magnetic resonance and chemometrics

(2019) Xiaoying Song et al.   JOURNAL OF FOOD COMPOSITION AND ANALYSIS

Combining convolutional neural networks and on-line Raman spectroscopy for monitoring the Cornu Caprae Hircus hydrolysis process

(2019) Xu Yan et al.   SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

Untargeted headspace gas chromatography – Ion mobility spectrometry analysis for detection of adulterated honey

(2019) Natalia Arroyo-Manzanares et al.   TALANTA

Use of spectroscopic methods in combination with linear discriminant analysis for authentication of food products

(2018) M. Esteki et al.   FOOD CONTROL

Physicochemical characterization of Lavandula spp. honey with FT-Raman spectroscopy

(2018) Ofélia Anjos et al.   TALANTA

Honey: Single food stuff comprises many drugs

(2018) Shahid Ullah Khan et al.   SAUDI JOURNAL OF BIOLOGICAL SCIENCES

Rapid prediction of phenolic compounds and antioxidant activity of Sudanese honey using Raman and Fourier transform infrared (FT-IR) spectroscopy

(2017) Haroon Elrasheid Tahir et al.   FOOD CHEMISTRY

Application of analytical methods in authentication and adulteration of honey

(2017) Amna Jabbar Siddiqui et al.   FOOD CHEMISTRY

Composition and properties of Apis mellifera honey: A review

(2017) Adriane Alexandre Machado De-Melo et al.   JOURNAL OF APICULTURAL RESEARCH

Recent advancements in detecting sugar-based adulterants in honey – A challenge

(2017) Liming Wu et al.   TRAC-TRENDS IN ANALYTICAL CHEMISTRY

Classification of Honey According to Geographical and Botanical Origins and Detection of Its Adulteration Using Voltammetric Electronic Tongue

(2016) Madiha Bougrini et al.   Food Analytical Methods

Using sensor and spectral analysis to classify botanical origin and determine adulteration of raw honey

(2016) Zhilin Gan et al.   JOURNAL OF FOOD ENGINEERING

Detection of honey adulteration with starch syrup by high performance liquid chromatography

(2015) Shaoqing Wang et al.   FOOD CHEMISTRY

A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration

(2015) Santosh Lohumi et al.   TRENDS IN FOOD SCIENCE & TECHNOLOGY

Using DNA Metabarcoding to Identify the Floral Composition of Honey: A New Tool for Investigating Honey Bee Foraging Preferences

(2015) Jennifer Hawkins et al.   PLoS One

Identification of Acacia Honey Adulteration with Rape Honey Using Liquid Chromatography–Electrochemical Detection and Chemometrics

(2014) Jinmei Wang et al.   Food Analytical Methods

Detection of adulterated honey produced by honeybee (Apis mellifera L.) colonies fed with different levels of commercial industrial sugar (C3 and C4 plants) syrups by the carbon isotope ratio analysis

(2014) Ahmet Guler et al.   FOOD CHEMISTRY

Rapid analysis of sugars in honey by processing Raman spectrum using chemometric methods and artificial neural networks

(2012) Beril Özbalci et al.   FOOD CHEMISTRY