Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

Nondestructive Multivariate Classification of Codling Moth Infested Apples Using Machine Learning and Sensor Fusion

Apple is the number one on the list of the most consumed fruits in the United States. The increasing market demand for high quality apples and the need for fast, and effective quality evaluation techniques have prompted research into the development of nondestructive evaluation methods. Codling moth (CM), Cydia pomonella L. (Lepidoptera: Tortricidae), is the most devastating pest of apples. Therefore, this dissertation is focused on the development of nondestructive methods for the detection and classification of CM-infested apples. The objective one in this study was aimed to identify and characterize the source of detectable vibro-acoustic signals coming from CM-infested apples. A novel approach was developed to correlate the larval activities to low-frequency vibro-acoustic signals, by capturing the larval activities using a digital camera while simultaneously registering the signal patterns observed in the contact piezoelectric sensors on apple surface. While the larva crawling was characterized by the low amplitude and higher frequency (around 4 Hz) signals, the chewing signals had greater amplitude and lower frequency (around 1 Hz). In objective two and three, vibro-acoustic and acoustic impulse methods were developed to classify CM-infested and healthy apples. In the first approach, the identified vibro-acoustic patterns from the infested apples were used for the classification of the CM-infested and healthy signal data. The classification accuracy was as high as 95.94% for 5 s signaling time. For the acoustic impulse method, a knocking test was performed to measure the vibration/acoustic response of the infested apple fruit to a pre-defined impulse in comparison to that of a healthy sample. The classification rate obtained was 99% for a short signaling time of 60-80 ms. In objective four, shortwave near infrared hyperspectral imaging (SWNIR HSI) in the wavelength range of 900-1700 nm was applied to detect CM infestation at the pixel level for the three apple cultivars reaching an accuracy of up to 97.4%. In objective five, the physicochemical characteristics of apples were predicted using HSI method. The results showed the correlation coefficients of prediction (Rp) up to 0.90, 0.93, 0.97, and 0.91 for SSC, firmness, pH and moisture content, respectively. Furthermore, the effect of long-term storage (20 weeks) at three different storage conditions (0 °C, 4 °C, and 10 °C) on CM infestation and the detectability of the infested apples was studied. At a constant storage temperature the detectability of infested samples remained the same for the first three months then improved in the fourth month followed by a decrease until the end of the storage. Finally, a sensor data fusion method was developed which showed an improvement in the classification performance compared to the individual methods. These findings indicated there is a high potential of acoustic and NIR HSI methods for detecting and classifying CM infestation in different apple cultivars

Nondestructive measurement of fruit and vegetable quality

We review nondestructive techniques for measuring internal and external quality attributes of fruit and vegetables, such as color, size and shape, flavor, texture, and absence of defects. The different techniques are organized according to their physical measurement principle. We first describe each technique and then list some examples. As many of these techniques rely on mathematical models and particular data processing methods, we discuss these where needed. We pay particular attention to techniques that can be implemented online in grading lines

Sensors for product characterization and quality of specialty crops—A review

This review covers developments in non-invasive techniques for quality analysis and inspection of specialty crops, mainly fresh fruits and vegetables, over the past decade up to the year 2010. Presented and discussed in this review are advanced sensing technologies including computer vision, spectroscopy, X-rays, magnetic resonance, mechanical contact, chemical sensing, wireless sensor networks and radiofrequency identification sensors. The current status of different sensing systems is described in the context of commercial application. The review also discusses future research needs and potentials of these sensing technologies. Emphases are placed on those technologies that have been proven effective or have shown great potential for agro-food applications. Despite significant progress in the development of non-invasive techniques for quality assessment of fruits and vegetables, the pace for adoption of these technologies by the specialty crop industry has been slow

Non-destructive Detection of Food Adulteration to Guarantee Human Health and Safety

The primary objective of this review is to critique the basic concepts of non-destructive detection of food adulteration and fraud which collectively represent a tremendous annual financial loss worldwide and a major cause of human disease. The review covers the principles of the analytical instrumentation used for the non-destructive detection of food adulteration. Examples of practical applications of these methods for the control of food adulteration are provided and a comparative analysis of the advantages and disadvantages of instrumental methods in food technology are critiqued.Целью данного обзора является критическое рассмотрение основных понятий неразрушающего выявления фальсификации и подделки продуктов питания, которые в целом вызывают огромные ежегодные финансовые убытки во всем мире и являются одной из основных причин заболеваний человечества. Материалы и методы. Литература, указанная в данном обзоре, была получена в результате поиска библиографической информации в CAB abstracts, AGRICOLA, SciFinder Scholar, Modern Language Association (MLA), American Psychological Association (APA), OECD / EEA database по инструментам, которые используются для экологической политики и управления природными ресурсами, и Web of Science.Результаты и обсуждение. Фальсификация пищевых продуктов означает преднамеренное, обманное добавление посторонних, нестандартных или дешевых ингредиентов в продукты, или разбавление или удаление некоторых ценных ингредиентов с целью увеличения прибыли. В современных условиях производители стремятся увеличить выпуск своей продукции зачастую путем изготовления и продажи некачественных и фальсифицированных продуктов.“Неразрушающее выявление фальсификации пищевых продуктов” означает анализ образца и его существенных признаков без изменения физических и химических свойств образца. Повышение качества и безопасности пищевых продуктов путем разработки научных методов обнаружения фальсификации является главным условием для поддержания здоровья потребителей. Точная объективная оценка качества и выявление фальсификации пищевых продуктов представляется важнейшей целью пищевой промышленности. В связи с совершенствованием технологии фальсификации продуктов важно быть в курсе современных, самых точных методов контроля их фальсификации. С этой целью данный обзор рассматривает основные понятия выявления фальсификации продуктов питания, принципы устройств и возможные практические применения современных методов неразрушающего выявления фальсификации продуктов питания; сравнительный анализ преимуществ и недостатков инструментальных методов, используемых в пищевых технологиях. Каждый из рассмотренных методов обсуждается с точки зрения возможных различных консистенций продуктов – газов (свободного пространства вокруг продукта), свободно текущих жидкостей (соков), мутных и вязких жидкостей (меда как продукта растительного происхождения, растительных масел) и интактных продуктов (фруктов и овощей).Выводы. Результаты, освещенные в обзоре, рекомендуется использовать при контроле качества и безопасности пищевых продуктов.Метою даного огляду є критичний розгляд основних понять неруйнівного виявлення фальсифікації і підробки продуктів харчування, які в цілому викликають величезні щорічні фінансові збитки у всьому світі і є однією з основних причин захворювань людства. Матеріали і методи. Література, зазначена в даному огляді, була отримана в результаті пошуку бібліографічної інформації в in CAB abstracts, AGRICOLA, SciFinder Scholar, Modern Language Association (MLA), American Psychological Association (APA), OECD/EEA database щодо інструментів, які використовуються для екологічної політики та управління природними ресурсами, та Web of Science. Результати та обговорення. Фальсифікація харчових продуктів означає умисне, облудне додавання сторонніх, нестандартних або дешевих інгредієнтів в продукти, або розбавлення чи видалення деяких цінних інгредієнтів з метою збільшення прибутків. У сучасних умовах виробники прагнуть збільшити випуск своєї продукції найчастіше шляхом виготовлення та продажу неякісних та фальсифікованих продуктів. “Неруйнівне виявлення фальсифікації харчових продуктів” означає аналіз зразка і його істотних ознак без зміни фізичних і хімічних властивостей зразка. Підвищення якості та безпеки харчових продуктів шляхом розробки наукових методів виявлення фальсифікації є головною умовою для підтримки здоров’я споживачів. Точна об’єктивна оцінка якості і виявлення фальсифікації харчових продуктів представляється найважливішою метою харчової промисловості. У зв’язку з удосконаленням технології фальсифікації продуктів важливо бути в курсі сучасних, найбільш точних методів контролю їх фальсифікації. З цією метою даний огляд розглядає основні поняття виявлення фальсифікації продуктів харчування, принципи пристроїв і можливі практичні застосування сучасних методів неруйнівного виявлення фальсифікації продуктів харчування; порівняльний аналіз переваг і недоліків інструментальних методів, що застосовуються в харчових технологіях. Кожен з розглянутих методів обговорюється з точки зору можливих різних консистенцій продуктів - газів (вільного простору навколо продукту), вільно текучих рідин (соків), каламутних та в'язких рідин (меду як продукту рослинного походження, рослинних масел) і інтактних продуктів (фруктів і овочів). Висновки. Результати, висвітлені в огляді, рекомендується використовувати під час контролю якості та безпеки харчових продуктів

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers\u27 expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects\u27 attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods\u27 application in the detection and classification of insect infestation in fruits and vegetables

Machine Vision Systems – A Tool for Automatic Color Analysis in Agriculture

It was in the early 1960s when machine vision systems initiated researchers and developers have worked on building machines that perform tasks of acquisition, processing, and analysis of images in a wide range of applications for different areas. Currently, along with the new technological advances in electronics, computer systems, image processing, pattern recognition, and mechatronics, it has arose the opportunity to improve machine vision systems development with affordable implementations at lower cost. A machine vision system is the combination of several high-tech techniques, including both hardware and software, used to acquire, process, and analyze images on a machine, which contributes with a set of tools for the extraction of features, such as color and dimension parameters, texture, chemical components, disease detection, freshness, assessment, modeling, and control, among others. Based on former paragraphs, we could say that machine vision systems are appropriate to improve the actual agricultural systems making them more useful, efficient, practical, and reliable

Making sense of light: the use of optical spectroscopy techniques in plant sciences and agriculture

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.info:eu-repo/semantics/publishedVersio