Innovative non-destructive technologies for quality monitoring of pineapples: Recent advances and applications

[1]  K. Mollazade,et al.  Hyperspectral imaging for predicting the chemical composition of fresh-cut pineapple (Ananas comosus) , 2022, Acta Horticulturae.

[2]  Rui Li,et al.  Changes in taste and volatile compounds and ethylene production determined the eating window of ‘Xuxiang’ and ‘Cuixiang’ kiwifruit cultivars , 2022, Postharvest Biology and Technology.

[3]  K. Tu,et al.  Growth Prediction of the Total Bacterial Count in Freshly Squeezed Strawberry Juice during Cold Storage Using Electronic Nose and Electronic Tongue , 2022, Sensors.

[4]  Sai Xu,et al.  Nondestructive detection and grading of flesh translucency in pineapples with visible and near-infrared spectroscopy , 2022, Postharvest Biology and Technology.

[5]  G. García-Mateos,et al.  Convolutional Neural Networks for Estimating the Ripening State of Fuji Apples Using Visible and Near-Infrared Spectroscopy , 2022, Food and Bioprocess Technology.

[6]  A. Mujumdar,et al.  Multi-frequency power ultrasound as a novel approach improves intermediate-wave infrared drying process and quality attributes of pineapple slices , 2022, Ultrasonics sonochemistry.

[7]  M. Ferreiro-González,et al.  Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data , 2022, Sensors.

[8]  M. Ferreiro-González,et al.  Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data , 2022, Agronomy.

[9]  Yitian Zhu,et al.  A novel strategy for discriminating different cultivation and screening odor and taste flavor compounds in Xinhui tangerine peel using E-nose, E-tongue, and chemometrics. , 2022, Food chemistry.

[10]  P. Vaithanomsat,et al.  Simultaneous Monitoring of the Evolution of Chemical Parameters in the Fermentation Process of Pineapple Fruit Wine Using the Liquid Probe for Near-Infrared Coupled with Chemometrics , 2022, Foods.

[11]  S. Bhat,et al.  Artificial Intelligence-Based Real-Time Pineapple Quality Classification Using Acoustic Spectroscopy , 2022 .

[12]  Pinyo Taeprasartsit,et al.  Acoustic Sensing for Quality Edible Evaluation of Sriracha Pineapple Using Convolutional Neural Network , 2022 .

[13]  N. Hashim,et al.  Quality prediction of different pineapple (Ananas comosus) varieties during storage using infrared thermal imaging technique , 2022, Food Control.

[14]  Z. Kovács,et al.  Detection of Monilia Contamination in Plum and Plum Juice with NIR Spectroscopy and Electronic Tongue , 2021, Chemosensors.

[15]  Yenming J. Chen,et al.  Non-destructive acoustic screening of pineapple ripeness by unsupervised machine learning and Wavelet Kernel methods , 2021, Science progress.

[16]  Yibin Ying,et al.  Food and agro-product quality evaluation based on spectroscopy and deep learning: A review , 2021 .

[17]  Xin He,et al.  On line detection of defective apples using computer vision system combined with deep learning methods , 2020 .

[18]  Riyanarto Sarno,et al.  Optimizing Machine Learning Parameters for Classifying the Sweetness of Pineapple Aroma Using Electronic Nose , 2020 .

[19]  N. Hashim,et al.  Pineapple (Ananas comosus): A comprehensive review of nutritional values, volatile compounds, health benefits, and potential food products. , 2020, Food research international.

[20]  Efstathios Z. Panagou,et al.  Application of spectroscopic and multispectral imaging technologies on the assessment of ready-to-eat pineapple quality: A performance evaluation study of machine learning models generated from two commercial data analytics tools , 2020, Comput. Electron. Agric..

[21]  Mohamad Nur Hakim Jam,et al.  Pre-dispersive near-infrared light sensing in non-destructively classifying the brix of intact pineapples , 2020, Journal of Food Science and Technology.

[22]  Yongxin Li,et al.  A colorimetric sensor array based on natural pigments for the discrimination of saccharides. , 2020, Luminescence : the journal of biological and chemical luminescence.

[23]  P. Variyar,et al.  Microbial quality assessment of minimally processed pineapple using GCMS and FTIR in tandem with chemometrics , 2020, Scientific Reports.

[24]  Sai Xu,et al.  Rapid Nondestructive Detection of Water Content and Granulation in Postharvest “Shatian” Pomelo Using Visible/Near-Infrared Spectroscopy , 2020, Biosensors.

[25]  J. Meile,et al.  Changes of Quality of Minimally-Processed Pineapple (Ananas comosus, var. ‘Queen Victoria’) during Cold Storage: Fungi in the Leading Role , 2020, Microorganisms.

[26]  Adelakun Oluyemisi Elizabeth,et al.  Physiochemical and Organoleptic Evaluation of Drink Produced from Pineapple (Ananas comosus) and Tigernut (Cyperus esculentus) , 2020 .

[27]  Majid Lashgari,et al.  Fusion of acoustic sensing and deep learning techniques for apple mealiness detection , 2020, Journal of Food Science and Technology.

[28]  D. Barbin,et al.  Identification of turkey meat and processed products using near infrared spectroscopy , 2020 .

[29]  Maria Margarida Mateus,et al.  Fourier Transform Infrared (FT-IR) Spectroscopy as a Possible Rapid Tool to Evaluate Abiotic Stress Effects on Pineapple By-Products , 2019, Applied Sciences.

[30]  J. Verdeil,et al.  An Imaging Approach to Identify Mechanisms of Resistance to Pineapple Fruitlet Core Rot , 2019, Front. Plant Sci..

[31]  E. Teye,et al.  Feasibility Study of the Use of Handheld NIR Spectrometer for Simultaneous Authentication and Quantification of Quality Parameters in Intact Pineapple Fruits , 2019, Journal of Spectroscopy.

[32]  Electronic-Nose System for Classification of Fruits and Freshness Measurement using K-NN Algorithm , 2019, International Journal of Innovative Technology and Exploring Engineering.

[33]  Hélène Rogniaux,et al.  Ion Mobility Spectrometry in Food Analysis: Principles, Current Applications and Future Trends , 2019, Molecules.

[34]  Fidel Toldrá,et al.  Application of non-invasive technologies in dry-cured ham: An overview , 2019, Trends in Food Science & Technology.

[35]  S. R.,et al.  Physicochemical properties and sensory acceptability of pineapples of different varieties and stages of maturity , 2019, Food Research.

[36]  I. Muhamad,et al.  Evaluation of chilling injury and internal browning condition on quality attributes, phenolic content, and antioxidant capacity during sub-optimal cold storage of malaysian cultivar pineapples , 2019, Malaysian Journal of Fundamental and Applied Sciences.

[37]  Samuel Verdú,et al.  Laser backscattering imaging as a control technique for fluid foods: Application to vegetable-based creams processing , 2019, Journal of Food Engineering.

[38]  Vaishali,et al.  Pineapple (Ananas cosmosus) product processing: A review , 2019 .

[39]  Kwankamon Dittakan,et al.  Non-destructive Grading of Pattavia Pineapple using Texture Analysis , 2018, 2018 21st International Symposium on Wireless Personal Multimedia Communications (WPMC).

[40]  Mahdi Ghasemi-Varnamkhasti,et al.  Potential use of electronic noses, electronic tongues and biosensors as multisensor systems for spoilage examination in foods , 2018, Trends in Food Science & Technology.

[41]  Bartosz Bieżyński,et al.  Health-promoting properties of pineapple , 2018, Pediatria i Medycyna Rodzinna.

[42]  Daniel I. Onwude,et al.  Evaluation of Chilling Injury in Mangoes Using Multispectral Imaging. , 2018, Journal of food science.

[43]  Hongbin Pu,et al.  Emerging Spectroscopic and Spectral Imaging Techniques for the Rapid Detection of Microorganisms: An Overview. , 2018, Comprehensive reviews in food science and food safety.

[44]  Da-Wen Sun,et al.  Innovative nondestructive imaging techniques for ripening and maturity of fruits – A review of recent applications , 2018 .

[45]  Da-Wen Sun,et al.  Multispectral Imaging for Plant Food Quality Analysis and Visualization. , 2018, Comprehensive reviews in food science and food safety.

[46]  R. Curini,et al.  Non-aqueous reversed-phase liquid-chromatography of tocopherols and tocotrienols and their mass spectrometric quantification in pecan nuts , 2017 .

[47]  Siti Khairunniza Bejo,et al.  Rapid and nondestructive techniques for internal and external quality evaluation of watermelons: A review , 2017 .

[48]  Hyang Sook Chun,et al.  Modern analytical methods for the detection of food fraud and adulteration by food category. , 2017, Journal of the science of food and agriculture.

[49]  Fatimah Sham Ismail,et al.  Simulation and Segmentation Techniques for Crop Maturity Identification of Pineapple Fruit , 2017, AsiaSim 2017.

[50]  S. Delwiche,et al.  Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging , 2017 .

[51]  Yuan Fang,et al.  Correlation analysis between chemical or texture attributes and stress relaxation properties of ‘Fuji’ apple , 2017 .

[52]  Rosnah Shamsudin,et al.  RGB imaging system for monitoring quality changes of seedless watermelon during storage , 2017 .

[53]  Mohamad Nur Hakim Jam,et al.  A five band near-infrared portable sensor in nondestructively predicting the internal quality of pineapples , 2017, 2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA).

[54]  B. Sipes,et al.  Pests, diseases and weeds , 2016 .

[55]  F. HossainM World pineapple production: an overview , 2016 .

[56]  S. M. Silva,et al.  Using digital image processing for evaluation of translucency in fresh-cut ‘Pérola’ pineapple coated with biofilms , 2016 .

[57]  P. Vithu,et al.  Machine vision system for food grain quality evaluation: A review , 2016 .

[58]  K. Walsh,et al.  Internal defect detection in fruit by using NIR spectroscopy , 2016 .

[59]  Siow Lee Fong,et al.  Determination of Physicochemical Properties of Osmo-dehydrofrozen Pineapples , 2016 .

[60]  Jordi Garcia-Mas,et al.  Textural properties of different melon (Cucumis melo L.) fruit types: Sensory and physical-chemical evaluation , 2016 .

[61]  L. Angel,et al.  Assessing the state of maturation of the pineapple in its perolera variety using computer vision techniques , 2015, 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA).

[62]  Ali Hassanpour,et al.  Mealiness Detection in Agricultural Crops: Destructive and Nondestructive Tests: A Review , 2015 .

[63]  Eduardo García-Breijo,et al.  An Electrochemical Impedance Spectroscopy-Based Technique to Identify and Quantify Fermentable Sugars in Pineapple Waste Valorization for Bioethanol Production , 2015, Sensors.

[64]  Da-Wen Sun,et al.  Recent Applications of Spectroscopic and Hyperspectral Imaging Techniques with Chemometric Analysis for Rapid Inspection of Microbial Spoilage in Muscle Foods , 2015 .

[65]  Preecha Komkum,et al.  Pineapple quality grading using image processing and fuzzy logic based on Thai Agriculture Standards , 2015, 2015 International Conference on Control, Automation and Robotics.

[66]  Hailong Wang,et al.  Fruit Quality Evaluation Using Spectroscopy Technology: A Review , 2015, Sensors.

[67]  Anupun Terdwongworakul,et al.  Quantitative prediction of nitrate level in intact pineapple using Vis–NIRS , 2015 .

[68]  Umezuruike Linus Opara,et al.  Analytical methods for determination of sugars and sweetness of horticultural products—A review , 2015 .

[69]  Da-Wen Sun,et al.  Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review. , 2015, Comprehensive reviews in food science and food safety.

[70]  R. Carle,et al.  Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry , 2015, Analytical and Bioanalytical Chemistry.

[71]  Baohua Zhang,et al.  Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review , 2014 .

[72]  Chia Kim Seng,et al.  Analysis for Soluble Solid Contents in Pineapples using NIR Spectroscopy , 2014 .

[73]  N. E. Bari,et al.  E-Nose and e-Tongue combination for improved recognition of fruit juice samples. , 2014, Food chemistry.

[74]  Jun Wang,et al.  Detection of adulteration in cherry tomato juices based on electronic nose and tongue: Comparison of different data fusion approaches , 2014 .

[75]  Wei Tian,et al.  The Experimental Study on the Enhanced Evaporating Property of the Pineapple Juice by Ultrasound , 2014 .

[76]  Irwin R. Donis-González,et al.  Internal characterisation of fresh agricultural products using traditional and ultrafast electron beam X-ray computed tomography imaging☆ , 2014 .

[77]  Asnor Juraiza Ishak,et al.  Ripeness level classification for pineapple using RGB and HSI colour maps , 2013 .

[78]  S. Teerachaichayut,et al.  NONDESTRUCTIVE PREDICTION OF INTERNAL BROWNING IN PINEAPPLE USING TRANSMITTANCE SHORT WAVELENGTH NEAR INFRARED SPECTROSCOPY , 2013 .

[79]  Baohua Zhang,et al.  A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy , 2013 .

[80]  Di Wu,et al.  Colour measurements by computer vision for food quality control – A review , 2013 .

[81]  R. A. Rahim,et al.  Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave ne , 2012 .

[82]  Mahmoud Omid,et al.  Principles and Applications of Light Backscattering Imaging in Quality Evaluation of Agro-food Products: a Review , 2012, Food and Bioprocess Technology.

[83]  Nadya Hajar,et al.  Physicochemical Properties Analysis of Three Indexes Pineapple (Ananas Comosus) Peel Extract Variety N36 , 2012 .

[84]  Kamarul Hawari Ghazali,et al.  Classification of Fresh N36 Pineapple Crop Using Image Processing Technique , 2011 .

[85]  Li Li,et al.  A Real-Time Pineapple Matching System Based on Speeded-Up Robust Features , 2010, 2010 International Conference on Computational Intelligence and Security.

[86]  O. Martín‐Belloso,et al.  Aroma profile and volatiles odor activity along gold cultivar pineapple flesh. , 2010, Journal of food science.

[87]  Sara Limbo,et al.  Shelf life evaluation of fresh-cut pineapple by using an electronic nose. , 2010 .

[88]  Ning Wang,et al.  Development of a Real-Time Fruit Recognition System for Pineapple Harvesting Robots , 2010 .

[89]  Ernestina Casiraghi,et al.  Evaluation of shelf-life of fresh-cut pineapple using FT-NIR and FT-IR spectroscopy , 2009 .

[90]  W. Daud,et al.  Physico-Mechanical properties of the Josapine Pineapple Fruits. , 2009 .

[91]  R. Infante,et al.  Monitoring the sensorial quality and aroma through an electronic nose in peaches during cold storage , 2008 .

[92]  Asa Prateepasen,et al.  Fitting a Pineapple Model for Automatic Maturity Grading , 2007, 2007 IEEE International Conference on Image Processing.

[93]  David C. Slaughter,et al.  X-ray assessment of translucency in pineapple , 2006 .

[94]  P. Lasaygues,et al.  Non-destructive evaluation of firmness of fresh pineapple by acoustic method , 2001 .

[95]  J. Sornsrivichai,et al.  Nondestructive techniques for quality evaluation of pineapple fruits. , 2000 .