با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Accurate prediction of soluble solid content of apples from multiple geographical regions by combining deep learning with spectral fingerprint features
پیش بینی دقیق محتوای جامد محلول سیب از مناطق مختلف جغرافیایی با ترکیب یادگیری عمیق با ویژگی های اثر انگشت طیفی-2019 The geographical origin of an apple can affect its cellular structure, and therefore its optical properties including
interactions with incident light. As a result, accurate prediction of soluble solid content (SSC) in apples with
multiple geographical origins is still challenging. A multiple-origin SSC prediction model for apples from multiple
geographical regions has been developed by combining spectral fingerprint feature extraction, origin recognition,
model search strategies, optimal wavelength selection, and deep learning with multivariate regression
analysis. In this model, the spectral fingerprint features of apples were explored and determined using the
random frog algorithm, and deep learning was used to train and test for origin recognition with the fingerprint
spectral feature as inputs. Particle least squares (PLS) was applied to develop individual-origin calibration
models, and subsequently employed to detect SSCs. A competitive adaptive reweighted sampling (CARS) algorithm
was used to select the optimal wavelengths for the calibration models. Compared with the individualorigin
model, the proposed multiple-origin model achieved more accurate results for the prediction of SSC of
apples with multiple geographical origins, with the RP and RMSEP values being 0.990 and 0.274, respectively.
These results indicate that variations in geographical origin affect accuracy, but that the multiple-origin model
can eliminate the effects of geographical origin on SSC prediction, thereby improving the applicability of SSC
detection in practice. Keywords: Near-infrared spectroscopy | Spectral fingerprint features | Multiple-origin model | Soluble solid content | Deep learning |
مقاله انگلیسی |