## 3 Results and Discussion
### Classification Results between Normal and Defective White Radishes
Compared with full spectrum-based models, models based on select highly discriminant wavelengths (i.e., the best 10 and reduced 4 wavelengths) achieved slightly lower overall accuracy for discriminating between normal and defective white rablishes. However, models based on fewer wavelengths were more suitable for on-line or real-time applications, and the loss was only slight; for example, using four wavelengths (reduction of 99.5% of the input variables) only resulted in a 2.8% \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Wavelengths** & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Calibration (\%)**} & \multicolumn{2}{c}{**Prediction (\%)**} \\  & **Number** & & & **Accuracy** & **Overall** & **Accuracy** & **Overall** \\ \hline \multirow{4}{*}{PLS-DA 1} & 420 & LVs: 5 & Normal & 100 & 100 & 98.9 & 99.4 \\  & & & Black heart & 100 & & 100 & 99.4 \\  & & & Normal & 100 & & 100 & 99.4 \\  & 10 & LVs: 7 & Black heart & 99.4 & 99.7 & 98.9 & 99.4 \\  & & & Normal & 98.7 & 97.2 & 93.5 & 96.1 \\ \hline \multirow{4}{*}{SVM} & 420 & KT: RBF; C: 100; & Normal & 100 & & 100 & \\  & & \(\gamma\): 0.031623 & Black heart & 100 & & 97.8 & 98.9 \\  & 10 & KT: RBF; C: 100; & Normal & 100 & & 98.9 & 98.9 \\  & 10 & \(\gamma\): 31623 & Black heart & 100 & 100 & 98.9 & 98.9 \\  & 4 & KT: RBF; C: 100; & Normal & 98.7 & 97.2 & 97.7 & 95.6 \\  & & \(\gamma\): 0.0001 & Black heart & 95.7 & 97.2 & 93.5 & 95.6 \\ \hline \multirow{4}{*}{ANN} & 420 & HLAF: tangent; OLAF: & Normal & 100 & & 100 & 100 \\  & & softmax: HLN: 15 & Black heart & 100 & & 100 & 100 \\  & 10 & HLAF: Hyperbolic tangent; & Normal & 100 & & 100 & 100 \\  & 10 & OLAF: softmax; HLN: 6 & Black heart & 100 & & 100 & 100 \\  & 4 & HLAF: Hyperbolic tangent; & Normal & 99.4 & 98.9 & 98.9 & 97.2 \\  & 10 & OLAF: softmax; HLN: 3 & Black heart & 98.1 & 98.7 & 95.7 & 97.2 \\ \hline \multirow{4}{*}{FLDA} & 420 & - & Normal & - & - & - & - \\  & & & Black heart & - & - & - & - \\ \cline{1-1}  & 10 & - & Normal & 99.4 & 100 & 99.7 & 100 & 99.4 \\ \cline{1-1}  & 10 & - & Black heart & 100 & 99.7 & 98.9 & 99.4 \\ \cline{1-1}  & 4 & - & Normal & 99.4 & 100 & & 100 \\ \hline \hline \end{tabular}  
* PLS-DA: partial least squares discrimination analysis; SVM: support vector machine; ANN: artificial neural network; FLDA: [PERSON]’s linear discriminant analysis; LVs: Latent variables; KT: Kernel type; C: Cost; \(\gamma\): Gamma; RBF: radial basis function; HLAF: hidden layer activation function; OLAF: output layer activation function; HLN: hidden layers nodes.  
\end{table} 
Table 3: Classification results (%) for normal and defective rablishes with black heart based on transmittance using four models. decrease of overall accuracy for prediction based on ANN. With respect to different models, ANN, as a nonlinear model obtained the best discriminating performance not only using full wavelengths but also using selected wavelengths (i.e., 10 and 4) for both calibration and prediction. However, it had less overall accuracy than FLDA for prediction. For the on-line or real-time field, FLDA could have the most potential for discrimination of normal and black-heart white radiishes using just four wavelengths.