Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content.

01/2008; DOI: 10.1016/j.postharvbio.2007.09.019
Source: OAI

ABSTRACT Hyperspectral scattering is a promising technique for nondestructive sensing of multiple quality attributes of apple fruit. This research evaluated and compared different mathematical models for describing the hyperspectral scattering profiles over the spectral region between 450 nm and 1000 nm in order to select an optimal model for predicting fruit firmness and soluble solids content (SSC) of 'Golden Delicious' apples. Ten modified Lorentzian distribution functions of various forms were proposed to fit the spectral scattering profiles at individual wavelengths, each of which gave superior fitting to the data with the average correlation coefficient (r) being greater than 0.995. Mathematical equations were derived to correct the spectral scattering intensity and distance by taking into account the instrument response and individual apples' size. The 10 modified Lorentzian distribution functions were compared for predicting fruit firmness and SSC using multi-linear regression and cross-validation methods. The modified Lorentzian function with three parameters (representing scattering peak value, width and slope) gave good predictions of fruit firmness with r =0.894 and the standard error of prediction (S.E.P.) of 6.14N, and of SSC with r =0.883 and S.E.P.=0.73%. Twenty-one and 23 wavelengths were needed to obtain the best predictions of fruit firmness and SSC, respectively. This new function, coupled with the scattering profile correction methods, improved the hyperspectral scattering technique for measuring fruit quality.

  • Source
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Knowledge of textural properties is important for stakeholders in the food value chain including producers, postharvest handlers, processors, marketers and consumers. For fresh foods such as fruit and vegetable, textural properties such as firmness are widely used as indices of readiness to harvest (maturity) to meet requirements for long term handling, storage and acceptability by the consumer. For processed foods, understanding texture properties is important for the control of processing operations such as heating, frying and drying to attain desired quality attributes of the end product. Texture measurement is therefore one of the most common techniques and procedures in food and postharvest research and industrial practice. Various approaches have been used to evaluate the sensory attributes of texture in foods. However, the high cost and time consumption of organizing panelists and preparing food limit their use, and often, sensory texture evaluation is applied in combination with instrumental measurement. Objective tests using a wide range of instruments are the most widely adopted approaches to texture measurement. Texture measurement instruments range from simple hand-held devices to the Instron machine and texture analyzer which provide time-series data of product deformation thereby allowing a wide range of texture attributes to be calculated from force–time or force–displacement data. In recent times, the application of novel and emerging non-invasive technologies such as near-infrared spectroscopy and hyper-spectral imaging to measure texture attributes has increased in both fresh and processed foods. Increasing demand for rapid, cost-effective and non-invasive measurement of texture remains a challenge in the food industry. The relationships between sensory evaluation and instrumental measurement of food texture are also discussed, which shows the importance of multidisciplinary collaboration in this field.
    Food Research International 01/2013; 51:823. · 3.01 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Este trabalho apresenta uma revisão da literatura existente, mais recente, sobre visão computacional aplicada à inspeção de frutas e verduras. Foram analisadas as técnicas mais empregadas para estimar diversas propriedades relacionadas com a qualidade. Os objetivos das aplicações típicas de tais sistemas incluem a classificação, a estimativa da qualidade segundo características internas ou externas, o seguimento dos processos da fruta durante o armazenamento ou a avaliação de tratamentos experimentais. Em geral, um sistema de visão computacional não só pode substituir a inspeção manual, mas também melhorar suas capacidades. Conclui-se que os sistemas de visão computacional são potentes ferramentas para a inspeção automática da qualidade de frutas e verduras, e o desenvolvimento de sistemas deste tipo, adaptados à indústria de alimentos, é fundamental para adquirir vantagens competitivas.
    Brazilian Journal of Food Technology. 12/2013; 16(4):254-272.


Available from